Navigating Conflicting Views: Harnessing Trust for Learning
Authors: Jueqing Lu
Wray Buntine
Yuanyuan Qi
Joanna Dipnall
Belinda Gabbe
Lan Du
Venue: ICML 2025 poster (2025)
Keywords: Multi-view Learning
Conflict Multi-view Learning
Reliable Multi-view Learning
Primary area: general_machine_learning

Abstract: Resolving conflicts is critical for improving the reliability of multi-view classification.
While prior work focuses on learning consistent and informative representations across views, it often assumes perfect alignment and equal importance of all views, an assumption rarely met in real-world scenarios, as some views may express distinct information. 
To address this, we develop a computational trust-based discounting method that enhances the Evidential Multi-view framework by accounting for the instance-wise reliability of each view through a probability-sensitive trust mechanism.
We evaluate our method on six real-world datasets using Top-1 Accuracy, Fleiss’ Kappa, and a new metric, Multi-View Agreement with Ground Truth, to assess prediction reliability. 
We also assess the effectiveness of uncertainty in indicating prediction correctness via AUROC.
Additionally, we test the scalability of our method through end-to-end training on a large-scale dataset.
The experimental results show that computational trust can effectively resolve conflicts, paving the way for more reliable multi-view classification models in real-world applications.
Codes available at: https://github.com/OverfitFlow/Trust4Conflict

Navigating Conflicting Views: Harnessing Trust for Learning
Jueqing Lu
Wray Buntine? Yuanyuan Qi'
Joanna Dipnall
Belinda Gabbe
Lan Du
Abstract
Resolving conflicts is critical for improving the re- liability of multi-view classification. While prior work focuses learning consistent and infor- mative representations across views, it often as- sumes perfect alignment and equal importance of all views, an assumption rarely met in real- world scenarios, as some views may express dis- tinct information. To address this, we develop computational trust-based discounting method that enhances the Evidential Multi-view frame- work by accounting for the instance- wise reliabil- ity of each view through a probability-sensitiv trust mechanism. We evaluate our method on six real-world datasets using Top-1 1 Accuracy, Fleiss' Kappa, and a new metric, Multi- View Agreement with Ground Truth, assess prediction reliabil- ity. We also assess the effectiveness of uncer- tainty in indicating prediction correctness via AU- ROC. Additionally, we test the scalability of our method through end-to-end training on a large- scale dataset. The experimental results show that computational trust can effectively resolve con- flicts, paving the way for more reliable multi-view classification models in real-world applications. Codes available at: https: ggithut........ com/ OverficFiow/TruustCConnii
Safe
Unsafe
Safe
Unsafe
(0.75, 0.05, 0.20)
(0.85, 0.10, 0.05)
TITANIC
Safe
Unsafe
(0.05, 0.05, 0.90)
(safe's baliof, uncertainty, unsafe's belief)
Figure I. Example of conflicting multi-view opinions. The Ti- tanic's route is safe in Captain's 's and Polar Bear's View, while unsafe Dolphin's 's view.
MVC rely the assumption that data from different views consistently provide reliable information about the ground truth (Liang et al., 2024; Zhang et al., 2023a; Xu et al., 2024a). Nevertheless, this assumption may not always be valid in real-world scenarios. Substantial variations in the informativeness of data from different views can produce conflicting results, thereby undermining the reliability of the model's 's predictions.
A possible solution for resolving conflicts is to project data from different views into a shared latent space (Hardoon et al., 2004; Wang et al., 2015; Federici et al., 2020; Hjelm et al., 2019), and then draw a joint representation from the latent space for the classification task. This is achieved by integrating essential features via weighting schemes, such as attention mechanisms (Zheng al., 2021) and weighted fu- sion (Atrey et al., 2010; Zhang et al., 2019). These methods typically assign higher weights to more informative views or features, thus reducing the impact of potential conflicting information. Although these methods have achieved promis- ing results in MVC, their focus on the joint representation can be a limitation. Solely relying on the joint representa- tion hinders the capacity thoroughly grasp information provided by different views. In contexts such as ocean navi- gation, characterized by observations sources from various views (e.g.. the perspectives of the captain, dolphin and Po- lar Bear when observing an iceberg as shown in Figure 1), it is crucial to thoroughly analyze and comprehend each
1. Introduction
Multi-View Classification (MVC) plays a critical role in deep learning by greatly enhancing the ability make ac- curate decisions through integrating multi-source informa- tion. Its effectiveness has been verified with the successful application in many domains such autonomous driving (Yurtsever et al., 2020) and Al-assisted medical diagnostic systems (Kang et al., 2020). Most of the existing studies
'Department of Data Science & AI, Monash University CCollege of Engineering and Computer Science, VinUniversity "Scoool of Public Health and Preventive Medicine, Monash Uni- versity. Correspondence to: Lan Du Du monash.edu>>.
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Navigating Conflicting Views: Harnessing Trust For Learning
view before making the decision cross and face or de- tour, different views provide unique and complementary information.
specific predictions. In addition, our method can also enhance the consistency among opinions derived from different views.
Existing approaches to resolve conflicts build neural net- works to generate view-specific predictions and then com- bine view-specific predictions together. As a prime exam- ple, the Evidential Multi-view framework (Han et al., 2021) emerging as a promising approach, offering reliable means for the final fusion stage. Within this framework, evidence acts as a metric of endorsement for the associ- ated predicted label, and the evidence is collected through view-specific neural networks. Subsequently, evidence from diverse viewpoints is fused, considering their respective epistemic uncertainties. However, there may exist cases where the view-specific information is not well aligned with the ground truth, resulting in misleading predictions with high confidence (low uncertainty). For example, as shown in Figure 1, while the dolphin can clearly observe the massive structure hidden beneath the water's surface, the captain may only see the tip of the iceberg.
2. Related Work
Multi-Vier Classification leverages multiple data sources, offering varied perspectives on the same object, to enhance the classification performance. Recent advancements in MVC have focused generating noise-robust representa- tions through cluster-based (Huang et al., 2023; Wen et al., 2023a; Zhang et al., 2023b), self-representationn based (Hou et al., 2020), and partially view-aligned (Wen et al., 2023b; Huang et al., 2020) methods, harnessing the expressive power of deep neural networks. However, noise-robust rep- resentations may not fully resolve conflicts in opinions for given data instance, conflicts may arise by discrepant information from distinct views, and the discrepancy cannot be eliminated by addressing noises. Our method addresses this limitation by introducing a separate evidence network that evaluates the reliability of view-specific predictions and adjusts the final predictions according to the degree of trust.
In this work, we take significant step further: leveraging the Evidential Multi-view framework, we propose a new computational trust based opinion fusion method to resolve potential conflicts in MVC. Specifically, the computational trust is modeled through an evidence network that operates on a view-specific and instance-wise basis. Drawing upon the principle of trust discounting in subjective logic, it eval- uates the reliability of view-specific predictions generated by existing Evidential frameworks, such as Evidential Deep Learning (EDL) (Sensoy et al., 2018). Within the proposed method, each view-specific evidence is transformed into degree of trust using the Binomial opinion theory (Jøsang, 2018). These degrees of trust are then utilized to establish uncertainty and a trust-aware opinion, ultimately facilitat- ing the generation of reliable predictions. In summary, the contributions of this paper include:
Trusted Multi- Multi-View Classification has emerged as a cru- cial area and pivotal domain within Multi- View Learning, This research area aims to enhance the accuracy and de- pendability of classification models by integrating data from multiple views, guided by their prediction confidence and epistemic uncertainty. The seminal work, Trusted Multi- View Classification (TMC) (Han et al., 2021), introduced the fusion of different views from an opinion perspective using the Dempher-Shafe Shaffer Combination rule. Building upon TMC, Han et al., 2022 extended the approach by incorpo- rating the pseudo-view, a concatenation of all other views, resulting in improved performance. Subsequent studies by Liu et al., 2022 and Xu et al., 2024a explored alternative opinion fusion methods. Concurrent research efforts, such as those by Jung et al., 2022 and Jung et al., 2023, focus multiview uncertainty estimation, enhancing the model's reliability. Recently, TEF (Liang et al., 2025) proposed evo- lutionary fusion to enhance pseudo-view quality in Trusted Multi- View Classification. Similar to the TMC, our method is also built upon the Evidential Neural Network (ENN), but with a novel Trust Discounting module integrated, which ad- just the original evidence and opinions before the Dempher- Shafer Combination based on the reliability of evidence and opinion.
1. We present novel learnable cust-discounting mecha- nism to extend the widely-used Evidential MVC frame- work, enhancing its conflict resolution capabilities. Drawing from the Binomial opinion theory within subjective logic, it operates on a view-specific and instance-wise basis, adeptly resolving conflicts among views through a cubbability-sensitivv trust discounting rule;
2. We develop stage-wise training strategy to optimize the parameters of the proposed mechanism, which works robustly on different datasets;
Conflictive Multi- Classification argues that existing work primarily focusing on either learning joint aligned rep- resentations or better quantifying uncertainty overlook the problem of potential contradictory in the prediction space. Recognizing this gap, the pioneer work by Xu et al., 2024a highlighted this issue and introduced the Degree of Conflict loss. This loss quantifies the disparity between different
3. We conduct extensive experiments on six real-world datasets, showing that our method outperforms the existing Evidential MVC methods, particularly on the datasets exhibiting large discrepancy among view-
2

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Navigating Conflicting Views: Harnessing Trust For Learning
predictions in the prediction space while accounting for uncertainty, aiming to mitigate conflict-related challenges. However, this approach may inadvertently lead correct pre- dictions to converge towards incorrect ones, potentially jeop- ardizing model stability. In the case, if most views are mak- ing incorrect predictions, the minority of correctly predicted views may be forced to align with the majority of incorrect ones. In contrast, our method can generate more accurate predictions with properly estimated uncertainty. As the trust discount module of our method is trained based on the cor- rectness of the view-specific prediction and directly assess the reliability of it, instead of using other views's 's predictions which may provide incorrect optimization direction.
trusted MVC. Other fusion rules, such as Aleatory Cumula- tive Belief Fusion (A-CBF) (Liu al., 2022) and Averaging Belief Fusion (ABF) (Xu et al., 2024a) have also been ex- plored. We choose to stay with BCF in our experiments due to its intuitive foundation (Jøsang et al., 2013; Jøsang, 2018) 8) and the effectiveness demonstrated by Han et al., 2021; 2022.
The fusion rule, @ of BCF, among two views, i.c, w wl a w? can be formulated as follows:
1 b% I-C
(b) by + -
+bbu))
u == --I-C - n'u (2)
where C= 0,b the normalization factor, and by is the belief mass of label l and is the uncertainty the fused opinion w. Since the order of combination does not affect the final result (Jøsang, 2018). applying Eq. 2 by sequentially combining the V views in pairs, where the result of each combination is then combined with the next view, will derive the final fused opinion, which is as follows,
3. Trust Fusion Enhanced Evidential MVC
3.1. Preliminaries
Given training data D = 1. where N is the number of training data, each instance æ; has V views, ground truth label y; and an one-hot encoded label y: (i.c., for a K-class classification problem, Yi is 1 if A is the index of ground truth label for i-th instance, otherwise it 0). The task of MVC to learn a function f that maps {= y:.
s w w²
(3)
For the fused opinion w, we can derive the parameters of the Dirichlet og by reversing the computation of Eq. 1.
Corollary 3.1. An alternative representation for BCF is based on combining the evidence ,from which the opinion w = [b. u, a) can be derived:
The Evidential MVC framework applies Subjective Logic (SL.) to the K-class classification problem by assigning belief masses to individual class labels and computing epis- temic uncertainty for the generated belief masses. The for- mulation links the evidence collected from instance view- specific observation to the concentration parameter of the Dirichlet Distribution. Let (-) denote the view-specific neural network for evidence generation, where the view- specific evidence for an instance is e" = fä (x=), the asso- ciation between the evidence and the Dirichlet parameters is simply og = ex + (Sensoy et al., 2018; Han et al., 2021). The belief mass on class label k, denoted as 6x. and uncertainty u are subject to the additive requirement, i.e.,
EN = el + e + eee
(4)
K
3.2. Conflict Resolving By Trust Fusion
We realize conflicts can happen when view-specific opin- ions express conflicting preferences, leading to ambiguity in the fused opinion, for example, two views' candidate labels has same confidence(belief), and subsequently draws potential inaccurate predictions. Based upon this, we define the conflict problem as follows:
Σ by 1. With respect to MVC, the view-specific belief mass by and uncertainty u" can then be computed as
Definition 3.2 (Conflicts within Multi-view Classification).
In a K -class multi-class classification problem involving a multi-view dataset, classification conflict arises when mul- tiple views that predict different classes. This conflict leads to ambiguity in aggregating these predictions, as it becomes challenging to determine a single, coherent classification result from those inconsistent predictions.
K
ex Sa
0% - SO 3
K
S"
S
1=1
(1)
To generate the final prediction, SL models the view- specific predictions as multinomial opinions, denoted as w"= = [b", u",, a"), with a" being the base rate (i.e.. a prior probability distribution over classes, generally discrete uni- form distribution), and then combine them together with an appropriate belief fusion rules based on the context (Jøsang et al., 2013). The Belief Constraint Fusion (BCF) (Jøsang et al., 2013), an extension of Dempher-Shar Shafer combination rule (Shafer, 1976), was first adopted by Han et al., 2021 in
Although Belicf Fusion has been verified effectively to fuse different opinions under SL, it still can generate unreliable fused opinions and lead to inaccurate predictions, for exam- ple, the Titanic navigation route case used in Figure 1. The data of different views' opinions have been recollected, and
'We provide the proof in Appendix C.2 and we implement BCF based on this equation due to its computational efficiency.
3

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Navigating Conflicting Views: Harnessing Trust For Learning
Table 1. Opinions from Different views and BCF Fused opinion
Table 2. Referral Opinions of Different views
(bllef(b)
Uncertainty(u)
Belief(b)
Uncer- DoT(p))
View
Safe
Unsafe
View
Trust
Distrust
tainty(u)
Captain(funnctioaal
0.85
0.05
0.10
Captain(referra)
0.6
0.3
0.1
0.65
Celphin((unctionan)
0.05
0,90
0.05
Dolphin(referra))
0.9
0.0
0.1
0.95
PolarBear(uunctional)
0.75
0.20
0.05
PolatBear(refferal)
0.2
0.7
0.1
0.25
Fused (via BCF)
0.68
0.31
0.01
Table 3. Discounted Opinions from Different views and BCF
shown in Table 1. Besides, we also compute the fused opin- ion generated through BCF by substituting the data of three (i.e., Captain, Dolphin and PolarBear) functional opinions into Eq. 2 and Eq. 3, and the fused opinion has also been appended to the Table 1.
Fused opinion
Beliet(()
Uncertainty(w)
View
Safe
Unsale
Captain[
0.55
0.03
0.42
(iscoutdd)
0.04
0,86
0.10
0.19
0.05
0.76
From Table 1, we can see that compared to the "unsafe" option, the fused opinion assigns higher belief mass to the "safe" option (0.68 vs. 0.31). As a result, the prediction will be "safe", which is factually incorrect, as indicated in Figure We attribute this error to insufficient evidence being col- lected, resulting in less belief mass supporting the factually correct option, "unsafe," in the opinions of both Captain and PolarBear. Additionally, the fused opinion exhibits lower uncertainty (0.01) compared to the original views' opinions (0.1, 0.05 and 0,05), however, the uncertainty is expected to be higher than that of all views to reflect the struggle among different opinions in the presence of conflict.
Fused (BCF)
0.22
0.70
0.08
BCF by:
@e = W @ of 1 ... ew
(w) @ ( @ (w) @ (a/ af) (6)
Note that 1) the referral opinion is different from the func- tional opinion shown Table 1, which aims for assessing reliability of corresponding views" functional opinion, and 2) comparing with original Probability-Sensiiivee TD (Jøsang et al., 2012), our proposed instance- wise manner takes into consideration the opinions reliability of each instance, in- stead of global reliability of view only.
We utilize the principle of Trust Fusion (TF) by Trust Dis- counting (TD) (Jøsang et al., 2015) to handle the incorrect prediction caused by conflicting opinions. The basic idea of TD is to discount evidence or opinion from an individual view as a function of trust on that view. It can be used to weigh the current view-specific opinion according to the degree of trust, thus guiding the fusion process to generate more reliable prediction. Here we present a Probability- sensitive Trust Discounting rule, as show in Eq. 5, and use it in an instance-wiss wise manner in our experiments as follows,
According to Jøsang et al., 2015, the probability Pr can be computed by μr = b, + ä, * with being the uniformly distributed base rate, i.c., ã 1/2 for each individual in- stance on each view. Assuming have the referral opinions for each view's functional opinion in Table 1, and defined in the Table 2. By substituting trust scores ÿe with the data in Table 2 and functional beliefs with the data in Table I in Eq. 5 and Eq. 6, we effectively apply TD to original functional opinions. This process enabled us to compute the discounted opinions for each view as well as their fused opinion through BCF combination, which is shown in Table 3.
Definition 3.3 (Instance-wise
Soobability-Sensiiee Trust Discounting). For each view of each individual instance, the trust-discounted opinion is defined as
{ 6 = Pf. * 6%:
wy- = of 3 of
"050 = pi,, * (IK.
(5)
We can see that with the intervention of TD, the BCF fused opinion now assigns more belief mass to "unsafe," which aligns with the factual label. Additionally, the uncertainty of the fused opinion is now 0.08, which is rational given that Captain's and PolarBear's opinions have high uncertainty. Therefore, the decision aligning with Dolphin's opinion, which has significantly lower uncertainty than the others, is reasonable.
where i, u are the index for w-th view of i-th instance, & indicates the TD operator, 0 denotes the discounted opinion, and W. á denote referral opinion and functional opinion (c.g., opinions in Table 1), respectively. The scalar proba- bility P. denotes the Degree of Trust (DoT), representing how much we are confident with the opinion given by the view-specific evidential model. Given Eq. 5, we fuse the trust-discounted opinions from V views of i-th instance with
Corollary 3.4. Above Eq. 3.3 also corresponds to updating
We prove that P. = by ag uu is equivalent to Pt 02/(a1 + a2) with the assumption that hase rate o, is uniformly distributed in Appendix C.I.
Definitions different opinion can he found in Appendix A.

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Navigating Conflicting Views: Harnessing Trust For Learning
View-1 Module
View-1 Opinion
Belief Fusion
View-1 input
Text:
b
'Spinach'
View-V Module
View-V Opinion
Subjective Opinion
View-V input
@ [b ..., bg.0t
input
View-V Opinion
Text:
6
'Spinach"
Referral ENN
By
bg
Referral Opinion d== [ö by, ür]
Functional
ENN
6
62
60
Discounted Opinion
6
0n== = [b]
5g 4
Functional Opinion " [6 6k,"
Trust Discounting
Figure 2. The TF Enhanced Evidential MVC Framework. The top half illustrates the overall pipeline of the Evidential MCV framework, while the bottom half zooms in to highlight the view-specific Trust Fusion. the Dirichlet evidence by
tion x and functional opinion 6 are used as inputs.
Pi, Gi
In terms of loss function, we follow Sensoy et al., 2018; Han et al., 2021; 2022; Xu et al., 2024a and optimize parameters of each view-specific evidential network. The loss term for i-th instance on v-th view is defined as follows,
6h
(7)
1 Pl, P. "6
The following propositions provide theoretical analysis of the proposed TD rule for achieving TF, and their detailed proof can be found in Appendix C.4.
K
Proposition 3.5. Instance-wise Probability- Senistivve TD maximizes the belief mass of the Ground truth label after BCF, under the assumption that at least one view's 's predic- tion correct.
L = Σ YLu(v(S)) (a(
k=1
+ D.D D Dir(p:
(8)
Proposition 3.6. The combined opinion generated by pro- posed TF (TD+BCF) for conflicting views, will exhibit greater uncertainty than obtained through fusion with non- discounted functional opinions.
where zó is the digamma function, A, mmmn110, o/10) is the annealing factor, and o the index the current training epoch, à y (1 y) Q is the Dirichlet parameters after removing misleading evidence from predicted distribution parameters and p is the projected probability, i.e., p a/S.
3.3. Learning to Form Opinions
We depict the proposed TF (TD+BCF) along with entire Evidential MVC framework Figure 2. The view-specific functional evidence is generated through an Evidential Neu- ral Network (ENN), i.e., é, (=)"), which same as (Han al., 2021). Similar to the functional evidence gener- ation process, construct another view-specific evidential network parameterized by é, for collecting referral evidence è, i.c., ~." f [<x]" 6%1)= 5, where both feature representa-
Note that, 1) the loss term above is directly linked with the distribution parameters that are generated through ENN parameterized by 0, which will also be updated through paok-propngatton during training stage; 2) even though we omit the notation for distinguishing the distribution param- eters that govern the variational transformation of referral and functional opinions, this loss term will still be applied to the referral and functional nets respectively: 3) the above equation will be also applied to the final fused opinion since its corresponding variational Dirichlet has parameter ā as well. We illustrate when and how to use the loss term in our
"We provide the proof in Appendix C.3. 'We use Bi-L Bi-Liincar layer instead of Dense/Lines Layer in experiments.
5

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Navigating Conflicting Views: Harnessing Trust For Learning
Algorithm 1 Algorithm For Training (simplified version)
4. Experiment
Input: Multi-view dataset D ( 1 Di) Initialize: The parameters 6, 0 of Functional and Referral ENNs, respectively.
4.1. Experimental Setup
Datasets. Following previous work (Han et al., 2021; 2022; Jung et al., 2022; Xu et al., 2024a), we conducted experi- ments on six benchmark datasets: Handwritten', Caltech101 (Fei-Fei al., 2004), PIR 8, Scene (Fei-Fei & Perona, 2005), HMDB (Kuehne et al., 2011) and CUB (Wah et al., 2011) with train-test split of 80% vs. 20%. A detailed description of these datasets is provided in the Appendix, we direct readers to the Appendix D.2 for further details regarding these datasets.
Stage-1 Warm-up Referral Network
Obtain {ē"} V + Referral ENNs outputs and {a"}", V Update the parameters θ by Gradient Descent (GD) with loss of Eq. 10 for all {ä"}";
Stage-2 Update Functional Network
/*Substage-2a* Obtain {é") V < Functional ENNs outputs and (á²));
Update the parameters á by GD with loss of Eq. 8 for all (d")":
Compared Methods. We aim to resolve conflicts among predictions of different views, so we consider the methods that generate view-specific predictions which could have potential conflicts, and thus consider existing Evidential MVC baselines, TMC (Han et al., 2021), and the conflict resolution pioneering work ECML (Xu et al., 2024a). Re- cent work, TMNR (Xu et al., 2024b) applied Evidential MVC for noisy label learning. and CCML (Liu et al., 2024) derived consistent evidence among shared information by dynamically decoupling the consistent and complementary evidence 2. Our method can also be extended to leverage the pseudo view, as demonstrated by its application to ETMC (Han et al., 2022), an extended version of TMC that incorpo- rates pseudo views. We also compare with one multi- uncertainty estimation baseline, MGP (Jung et al., 2022), in our experiments. We term our methods as TF and ETF where E indicates the pseudo-view is incorporated. All methods were run on single 24GB RTX3090 card for fair comparison.
/SSubstage22b* Obtain {ē") V
<- Referral ENNs outputs and (x*)" V
Obtain {é"}V V Functional ENNs outputs and (á**);
Obtain C" and 66 by Eq. 1 with ě" and é" for all views; Obtain BCF fused opinion @ by Eq. 6 and ā by Eq. I;
Update the parameters 6 by GD with loss of Eq. 8 for oz; Stage-3 Adjust Referral Network
By repeating Stage-2b and update Ø instead of é only: Stage-4 Adjust Functional Network
By repeating entire Stage-2: Output: Funetional and Referral networks parameters.
proposed stage-wise training algorithm ( (Algorithm 1)% 6
We also adopt a warm-up stage for the referral nets since the randomly initialized parameters of them could introduce unreliable trust scores for discounting at early training stage. The loss term used at the warm-up stage is simply the left summation term of Eq. 8 with a different target label which is defined as
Evaluation Metries. We evaluate MVC methods based on the reliability from prediction accuracy of fused opin- ion and the consistency among different views predictions. Similar to (Han et al., 2021; 2022; Jung et al., 2022; Xu et al., 2024a), we measure the prediction accuracy using Top-1 Classification Accuracy which checks whether the final predicted label of fused opinion is same as ground truth. Regarding to the consistency among various views' predictions, we apply the Fleiss Kappa (Fleiss, 1971), which is statistical measure for assessing the agreement between different raters, with scores closer to 1 indicating higher agreement among the different predictions. The intuition behind using this two metrics is reliable prediction should not be accurate only but also from most agreements.
1
if = otherwise
=
2
(9)
where of arg max; b which is predicted label of func- tional opinion, so the target label zı' primarily indicates the correctness of such prediction. Following Müller et al., 2019, we apply label smoothing with smoothing factor n = 0.9 to the hard label. The association between one- hot encoded hard label z of target 2/ and smooth label is by- = z @ n + (1 22. since the smoothed label could provide training signals for neurons of both target and non-target labels, we omit the KL term here. The summa- tion term, with Beta distribution parameters ö of referral opinion, changes to follows,
ics wwt.leedddmndaaaaaeeeeeeeee Mu fuitippleDeatures
2 Σ b (v(a + -
"nttp:/ cs. cmu. wwwp/t//cs/ppoo/e/eeee P PIE/MultiPie WitialtiPPeeMuuiiiPPPe PieeHomee...eeeeeeeeeeeeeeeeeeee html
(10)
"We re-run the official implementation of ECML, TMNR, CCML with our data loader to ensure a fair comparison.
'Due to space limitation, we provide a simplified version of training algorithm here for improving the readability and direct readers to Appendix B for the detailed training algorithm.
6

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Navigating Conflicting Views: Harnessing Trust For Learning
Table Top-I accuracy test split. The best results are highlighted in bold and the second-hest results are underlined.
Method
Handwritten
Co
PIE
Scenel5
HMDB
CUB
AVG
MGP
99.60#0.1 10
94.42=0.20
90.13+0.87
74.30=0.41
73.97=0.15
90.79+1.03
87.03
ECML
99.57=0.11
94.25=0.08
91.40=0.47
64.3420.11
72.90=0.11
92.58=0.25
85.84
TMNR
99.7240.08
94.31=0.09
89.3420.59
74.14
73.4640.15
92.25+0.38
87.21
CCML
99.00+0.00
94.6420.10
93.0920.36
13.97=0.15
72.5940.42
93,83+0.41
87.91
TMC
99.6340.13
94.30=0.13
87.4340.90
13.99=0.19
73.3040.18
92.50+0.37
86.60
ETMC
99.75+0.00
94.41=0.11
91.69+0.47
78.41+0.20
74.01+0.19
93.67=0.41
88.74
TF (ours)
99.68+0.11 99.98+0.07
95.26+0.10
93.31=0.40
77.83=0.32
74.35+0.09
93,33+0.75
88,
ETF (ours)
95.07=0.08
94.6340.34
82.01=0.17
75.55=0015
94.08+0.38
90.22
Table 5. Fleiss' Kappa on test splits. The best results are highlighted in bold and the second-best results are underlined.
Dataset
Handwritten
1l
PIE
Scene15
HMDB
CUB
AVG
MGP
0.59#0.05
0.94+0.00
0.21#0.01
0.33+0.00
0.51=0.00
0.43+0.07
0.50
ECML
0,42+0.05
0.95+0.00
0.40=0.01
0.26+0.00
0.53+0.01
0.44+0.07
0.50
TMNR
0,59:0.02
0.9420.01
0.29:0.02
0,3020.00
0.53+0.00
0.3720.06
0,50
CCML
0.6420.04
0.91+0.01
0.39#0.01
0.36#0.01
0.53+0.01
0.63+0.04
0.58
TMC
0.54+0.07
0.94+0.01
0.23+0.02
0.30#0.01
0.52+0.01
0.37+0.19
0.48
ETMC
0.66+0.01
0.84+0.00
0.28+0.04
0.37=0.00
-0.15=0.04
0.45+0.10
0.41
TF (ours)
0.65+0.02
0.95=0.00
0.3620.01
0.39+0.00
0,5420.00
0.5140.10
0.57
ETF (ours)
0.76+0.02
0.95=0.00
0.48+0.01
0.48+0.01
0.6520.00
0.6420.03
0.66
4.2. Experiment Results and Analysis
Predictions Consistency via Fleiss' Kappa. To further validate the effectiveness of our proposed method, we eval- uate it with Fleiss' Kappa (Fleiss, 1971). our methods (ETF and TF) achieves the highest Fleiss' Kappa score on all six datasets (Handwritten, PIE, 1cene HMDB and CUB). ETF enhances the robustness of ETMC with an improve- ment of approximately 13% on Caltech101. Moreover, it's essential to highlight that ETMC exhibits extremely poor agreement HMDB with negative value of -0.15. However, by applying our method, ETF significantly im- proves performance by an absolute value of 0.8. This under- scores the relative robustness of our method across different datasets.
For each individual metric, mean and standard deviation from ten runs with ten different random seeds are reported. In all tables, the best-performing method is highlighted in bold, and the second-best method is underlined.
Predictions Accuracy via Top-1 Accuracy. Similar to (Han et al., 2021; 2022; Jung al., 2022; Xu et al., 2024a), we first evaluated the model performance on the test split by Top-1 Classification Accuracy, shown in Table 4. Build- ing the strengths of pseudo view, our method (ETF) consistently outperforms all baselines over six datasets. For example, on the PIE and Scene 15 datasets, the use of referral trust boosts the accuracy of ETMC by 2.94% and 3.60%, re- spectively. Moreover, ETF surpasses the pioneering conflict resolving method ECML by substantial margin of 3.23% on PIE, 9.66% on Scene15 and 2.65% on HMDB, highlight- ing better power of conflicts handling of our method. It is worth noting that Caltech10) 101 inherently has lower level of conflicts, as corroborated by high accuracy and Fleiss' Kappa scores (Table 5) of all baselines.
Discussion on Consistency Improvement of Opinions from Different Views. It is worth noting that applying TD solely on existing functional opinions cannot improve the consistency among different views, however, our methods show that the consistency of opinions from different views is significantly improved, as measured by Fleiss Kappa. We attribute this improvement to the incorporation of TD in the training stage. The functional opinion will be discounted accordingly by the referral opinion, and it thus receive larger magnitude of gradients from the loss term, c.g., Algorithm I stage 2b, due to interactions between different opinions, e.g., Eq.2. Therefore, the functional opinion will be enforced to align with the ground truth which leads to the improved consistency among different views' opinions.
When compared to well-established methods like TMC, MGP, and ECML without pseudo views, our method TF consistently demonstrates superior performance across all datasets. For example, our proposed trust discounting method enhance TMC's performance by 3.84% on Scene 15 and 5.88% on PIE, while also achieving the highest Top-1 accuracy on other datasets. Notably, our method TF, even without incorporating pseudo views, exhibits comparable performance ETMC with pseduo views. For instance, TF outperforms ETMC on three datasets (Caltech101, PIE, and HMDB) out of total of six.
4.3. Ablation Study
Effectiveness of the TD module. We conducted the abla- tion study to validate the effectiveness of TD module on
7

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Navigating Conflicting Views: Harnessing Trust For Learning
Table Test Performance with without the TD module.
Table 8. Test Accuracy by using different views.
Method
Top-1 Acc(%)
Fleiss' Kappa
view 1
view
view 3
Top-1 Accuracy
ETF(W TD)
82.01
0,48+0,01
X
57.16=0.22
ETF(w/o TD)
81,06+0.16
0.46+0.01
X
x
75. 15.10
TF(w/ TD)
77.83+0.32
0.39+0.00
X
62.97=0.45
TF(w/o TD)
76,82+0.33
0.37+0.01
V
V
78.70=0.00 X
V
V
68.21=0.01
X
V
<
80.21=0.00
Table 7. Test Performance with Different Smoothing Factors.
V
V
02.01
Method
Top-1 I Acc(%)
Fleiss' Kappa
Table 9. Test Performance on Food101 via End2End training.
(TF,,.9, reported)
82.01
0.48+0.01
(TF().))
82.07=0.12
0.48+0.01
Method
Top-1 Acc
Fleiss' Kappa
ETF(0.8)
82.04+0.23
0,49=0.01
TMC
92.35=0.344
0.0377=0.0130
ETF(0.7)
82.07+0.10
0.48=0.01
ETMC
92.49=0.13
0.0252#0.0288
ETF(0.6)
81.96+0.16
0.47=0.01
ECML
92.53=0.15
-0.0207=0.0215
CCML
92.70=0.06
40.0342=0.022
TF (ours)
92.79+0.15
-0.0375=0,0255,
Scene15. In the case without the TD module, the correspond- ing training stages in Algorithm I related to TD module will be disabled, for example, the warm-up stage and training stage 2b.
ETF (ours)
93.09#0.02
0.0487#0.0228
4.4. End2End Training on Food101 Dataset
In order to further validate the effectiveness of our model, we use a larger dataset, Food101, which has both an image and text view. This is one dataset has the same number of class labels, 101, as Caltech101, and has more training (i.e., 6117), validation (i.e. 6845) and testing (i.e., 22716) instances. We train all methods using pre-trained Resnet50 and base-uncased Bert as image and text encoder, and we adopt Adam Optimizer for fine-tuning parameters. All other settings, e.g., maximum number of epochs, are identi- cal, and we run each method three times for reporting mean and standard deviation.
We can see from Table 6 that without the core module TD, the performance over four metrics drops, which indicates the effectiveness of our proposed TD module. is also worth noting that, without TD, the model architecture is almost identical TMC. However, both accuracy and Fleiss Kappa have improved, further demonstrating the effectiveness of our stage-wise training algorithm.
Various Smoothing Factors. We varied the smoothing factor used in the warm-up stage for ablation on 15. we set warm-up epoch equal to I, which is same as the reported results in the main text. The equation we used for smoothing hard label is Φ = xf !J + (1 - 12/ With a larger smoothing factor, the smoothed label becomes meaningless, so we varied the factor from 0.6 to 1.0 by step size 0.1. According to Table 7, we can see that our method is relatively robust to different smoothing factors, and even gains performance improvement with adjusted smoothing factors on Scene15 Dataset, e.g.. factor equals to 1.0, the smoothing factor used in Table 4 (i.e., 0.9) is the empirical value suggested in the original paper, to avoid hyper-parameters over-tuning.
As indicated in Table our method ETF consistently out- performs all other methods. Please note that TMNR is not included here as it requires pre-extracted feature vectors for computing similarity matrix, which works for noisy label learning and are kept frozen during training, but feature vectors are not able to be kept in this End2End training as the parameters of encoder will be updated.
5. Conclusion
In this paper, we introduced a theoretically ap- proach for resolving conflicts in Multi View Classification. This approach is built on top of the principle of the Trust Discounting in Subjective Logic, where the computational trust, aka referral trust, represented as a Binomial opinion with a Beta probability density function. The functional trust is then discounted by the amount computed as function of the degree of trust. We demonstrated through extensive experiments that the proposed trust discounting method not only benefits classification accuracy but also increases con- sistency among different views, providing a new reliable approach to handling conflicts in MVC.
Effectiveness of Leveraging Different Views. We use the Scene15 5 dataset as an example and ablate the number of views to evaluate the performance of the trust discounting mechanism under varying numbers of views. From Table we observe that the effectiveness of each individual view on classification varies significantly, reflected in the test accuracy of individual views. However, our method con- sistently improves accuracy by effectively incorporating different views. The highest accuracy is achieved when all views are utilized together, which proves the effectiveness of our method.
8

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Navigating Conflicting Views: Harnessing Trust For Learning
Acknowledgments
Han, Z., Zhang, C., Fu, H., and Zhou, T. Trusted multi- view classification. In International Conference on Learn- ing Representations, 2021.
We would like to thank the anonymous reviewers for their valuable and helpful comments.
Han, Z., Zhang, C., Fu, H., and Zhou, Trusted multi-
Impact Statement
view classification with dynamic evidential fusion. IEEE Iransactions on pattern analysis and machine intelligence, 45(2):2551-2566 2022.
This research introduces novel trust discounting mecha- nisms to address confficts across multiple data views, and ex- hance the Evidential MVC framework. By training using the proposed stage-wise algorithm, our method improves clas- sification accuracy and reliability in multiview data. These advancements have direct implications for applications in healthcare, autonomous systems, where reliability is critical. The findings provide foundation for future work on adap- tive trust mechanisms and conflict resolution in multiview classification system.
Hardoon, D. R., Szedmak, S., and Shawe-Taylor, Canon- ical correlation analysis: An overview with application learning methods. Neural computation, 16(12):2639- 2664, 2004.
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y. Learning deep representations by mutual information estimation and maximization. In International Conference on Learn- ing Representations, 2019.
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Navigating Conflicting Views: Harnessing Trust For Learning
A. The Definition of Opinions
Functional Opinion expresses belief model's own ability to perform certain task-such as classification task. reflects direct trust in the model's prediction. Let model A A be evaluated for its ability to perform a function (e.g.. classification). Then, a functional opinion is subjective opinion represented as:
= [b, ú, ál
A Referral Opinion, in contrast, expresses belief in a model's ability to provide reliable referrals regarding another model's ability to perform task. It reflects trust in the model's judgment, not in its own functional capability. Let model B be asked to refer another model A for the function f. A referral opinion captures our belief that model reliable in making referrals about anther's (i.e., model A's) ability to perform f, and denoted as:
b ü a
Regardless of whether the opinion is functional referral, b is the belief mass vector, ü is uncertainty score, with being the base rate (i.e., prior probability distribution over classes, generally discrete uniform distribution).
B. Proposed Algorithm For Training and Testing
Algorithm 2 Algorithm For Training
VV Input: Multi-view dataset: {(x; 1==1 11 J
Initialize: The parameters of the Functional networks; initialize the parameters 0 of the Referral networks. /Staage-1 Warm-up Referral Network*/ for minibatch do
for 1: do
t" <- Referral Evidential network batch output;
Obtain ã + +1;
end forObtain overall loss by summing losses calculated by Eq. 10 of all (á")V=
Update the parameters by gradient descent with the loss from above; end Stage2 Update Functional / for minibatch do MSSbstage-2a for 1: V do
6" + Functional Evidential network batch output;
Obtain á" + 11;
end for
Obtain overall loss by summing losses calculated by Eq. 8 of all (d')
Update the parameters by gradient descent with the loss from above; /*Substage-2b*/ for 1: do
e -- Referral Evidential network batch output; 6" f Functional Evidential network batch output;
Obtain " and " by Eq. 1 with ě" and é", respectively
end for
Obtain joint opinion @ by Eq. 6 and ā of this opinion by reversing Eq. 1;
Obtain loss by Eq. 8 with a and update the parameters é with gradient descent; end StageS Adjust Referral / By repeating Stage-2b only and update instead of Ó; /Saage-4 Adjust Functional //twork*
By repeating entire Stage-2;
Output: Functional and Referral networks parameters.
12

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Navigating Conflicting Views: Harnessing Trust For Learning
Algorithm 3 Algorithm For Testing
The parameters é of the Functional networks; the parameters 0 of the Referral networks. /*Tssting Phase"/ for minibatch do
Requires:
for 1 V do
è" - Referral Evidential network batch output; < Functional Evidential network batch output:
Obtain 3 and st by Eq. 1 with é" and é", respectively;
end for
Obtain joint opinion by Eq. 6 and á this opinion by reversing Eq. 1; Obtain predicted labels of minibatch using arg over belief masses.
end for
Output: Predicted Labels and Opinions including fused opinion, functional opinions, referral opinions, discounted opinions for each instance of each view.
C. Proofs And Derivations
C.1. Calculation of Predictive Probability
According to Subjective Logic (SL) (Jøsang, 2018), the predictive probability for class k, can be calculated by
Pk = by + *
(11)
where by is the belief mass for k-th label, u is the predictive uncertainty epistemic uncertainty (Sensoy et al., 2018). We usually assume the prior conforms to uniform discrete distribution, i.c. 1/K, so the above equation is identical to
a S
(12)
where og is the Dirichlet concentration parameter for k-tth label, and S is the Dirichlet strength, i.e., S Ex Ag.-
Proof.
=
by
ag
*
1
K
by
K
S
+
S
5
O
S
Since Beta Distribution is 2-dimensional Dirichlet Distribution, above equations for calculating probabilities of multinomial opinions could also be applied binomial opinions.
13

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Navigating Conflicting Views: Harnessing Trust For Learning
C.2. Alternative Representation of Belief Constraint Fusion(BCFF) Proof. We the proof for Eq. 4 as follows,
cx
S bk
5 - (bjb2 bluu² bl
S Ex by
(b/.2 + bluu² + =bzu))
u=u²
(S S Σ bk) u u2 (b + b/2u² u² + bgu
=
(S Σ 6k
(b bluu² B3
K u²
(bLbg + b/ + bzu')
Au Lu
K u' 5152
S1 $2
K( K x K
Ku²
Ku
de + c + t
K
C.3. Dirichlet Evidence Updating by Trust Discounting (TD)
As mentioned earlier, the TD in Definition 3.3 also corresponds to updating Dirichlet evidence using following equation,
pe ék
(13)
Pr
+ D.ú
where ie is the probability representing trust degree and á is the uncertainty for functional opinion. é is Dirichlet evidence of functional opinion, and é is Dirichlet evidence after discounting.
Proof.
by 5 B.b K
Brb K
1 11 + irú
1r.
ch K
1
P.
p.ú
5
K
ek
Pi + p.ú
Prú
Ck
1
Pt
P.ú
14

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Navigating Conflicting Views: Harnessing Trust For Learning
C.4. Detailed Proof of Propositions
Proof. Proof details of Proposition 3.5. Recall that scalar probability pr represents the degree of trust mentioned before. The belief mass for k-th label of final fused opinion is as follows,
by =
(6 + +
+6) bzpp(")
We g denote the index of ground-truth label, and we have
by = 1 C
The discounted opinion's uncertainty ä is
= - Pe( Σ 6x)
Pe ú
1 Pe. Pr. 1
In the warm-up training stage, the Eq. 10 is used to make sure Pt - (with hard targets simplicity here) for those views" predictions are same as the ground truth label, and ä 0 for those views" predictions are incorrect. Therefore, + when by max and ü 1 when by * mar(b).
Therefore, with the assumption that at least one-view's prediction is same the ground truth (i.e., correct label, let's say view I's prediction is correct), we have
b =
(1))))))))))))))))) 6,g + (3
2
1
((1)))
fpp + 12p (equality holds iif. k = g))
1
1
(61i2 i. (2 by
Besides the warm-up stage, in other training stages, such training stage 3 in Alg.B, the Pr will also be updated to maximize
By based on the Eq. 8, i.e., by 2 by (equaliity holds iif. k g. Therfore, the referral opinion is leartt to maximize the belief mass of ground truth label of the final fused opinion well.
Proof. Proof details of Proposition 3.6. Let and w/ denote the uncertainty of BCF combined opinion with or without Trust
15

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Navigating Conflicting Views: Harnessing Trust For Learning
Discounting, respectively.
K
6 0
k=
Σ
k=
+
2 +
b
+
DE +2
p
+
ΣE
646
k=
+
pf
PEPE
6169
81
+
k=
1(
K
61
69
k=
11
C.5. Loss Functions and Hyperparameters for Optimization
Recall that the probability density function (pdf) of the Dirichlet distribution, Dir(p ox), is given by:
K
Dir(p a)) B(a)
P3
1=1
where:
p = (Pi.P2....,PK' is a probability vector, such that E Ps and pk 2 for k.
a (a1, a2,...,,K ax) is a vector concentration parameters, with ag > 0.
((x) is the multivariate Beta function, defined as B(a)) = 11 T(mu)
<(CK )
T(-) is the Gamma function.
Recall that our loss function for Dirichlet Parameters a is
K
L
Σ (v,))) v(a(,)) +
[Dir[p][1]]
Specifically, the left summation term is derived from the Bayes risk for Cross-Entropy loss with a Dirichlet distribution, which is also dentoed as Laoe in previous work (Han et al., 2021). We omit the index of view v and instance i for simplicity, so Luce is defined as follows,
K
K
Luce /
yxlog(ps)
(px)
dp
B(a)
Σ yx(((S) ((ax))
(14)
Where & is the digamma function.
16

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Navigating Conflicting Views: Harnessing Trust For Learning
Recall that our referral network will generate the evidence for binomial opinion, and the evidence will be converted into parameters of Beta Distribution, i.e., Beta(os; 0n) Subsequently, by replacing the Dirichlet Distribution with Beta Distribution, and the label y/ in above equation with another label, can have the ace loss for Beta Distribution, as Eq. 10.
And the right term, KL divergence loss is
DEL [Di((p (x) Dir(p 1)
r kk= aske
K Σ /=1
log
ark 1)
(((x))) - 10
(15)
T(K)
I1 IK- I(ax)
k=1
D. Additional Details of The Experiment
D.1. Syper-parrametes Proposed Methods
The syper-parameters for training TF and ETF has been shown in Table 10. Concretely, "Ir" is the learning rate for functional networks, "rir" indicates the learning rate for referral networks. For the "Ir", we follow ETMC (Han et al., 2022). and used same strategy select learning rate for the functional nets. When tuning the learning rate for referral networks, follow basic principle of starting with value less than equal to the base learning rate, and then gradually decreasing the learning rate of referral network by factor of three. For fair comparison, we used same learning rate for functional networks for evidence-base methods, except MGP (Jung et al., 2022), for which we followed their paper.
Table 10. TF ETF hyper-parameters
Hyper-parameter
Handwritten
Caltech101
PIE
Scene15
HMDB
CUB
3e-3
le-4
3e-3
le-2
3e-4
le-3
rlr weight-decay
3e-4
3e-5
le-3
3e-3
le-4
3e-4
le-4
le-4
le-4
le-4
le-4
le-4
warm-up epochs
1
1
1
The Adam optimizer (Kingma & Ba, 2015) is used for updating model parameters with beta coefficients (0.9, 0.999) and epsilon 1e-8.
D.2. Summary of Dataset
Table 11. Summary of Datasets
Dataset
Size
K
Dimensions
#Train
#Test
HandWritten
2000
240/76/216/47/6446
1600
400
Caltech101
8677
101
4096/4096
6941
1736
PIE
680
68
484/256/279
544
136
1cene15
4485
15
20/59/40
3588
897
HMDB
6718
51
1000/1000
5374
1344
CUB
600
10
1024/300
480
120
We provide the summary of the dataset in Table 11, we direct readers to (Han et al., 2021) for further details regarding these classes. Each class is one of the digit 0 to with samples evenly distributed (i.e., 200 samples per class). We use six descriptors to represent different views, and they are Pixel averages in windows (Pix) feature with 240 dimensions, Fourier coefficients of the character shapes (FOU) with 76 dimensions, Profile correlations (FAC) features with 216 dimensions, Zernike moments (ZER) with 47 dimensions, Karhunen-Lowe coefficients (KAR) with 64 dimensions, and Morphological (MOR) features with 6 dimensions; 2) Caltech101 dataset has 101 classes and 8677 images in total; We used the extracted features from DECAF (Donahue et al., 2014) and VGG19 (Simonyan & Zisserman, 2015). Both views have 4096 dimensions. 3) PIE dataset includes intensity (484 dimensions). Local binary patterns (LBP) (256 dimensions) and Gabor feature (279 dimensions) of 680 facial images, with 68 subjects; 4) 15 dataset has 4485 images from 15 indoor and outdoor scene categories. There are
datasets. The datasets used our experiments are 1) Handwritten dataset has 2000 samples
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Navigating Conflicting Views: Harnessing Trust For Learning
different views information, and they are GIST, Pyramid Histogram of Oriented Gradients (PHOG) and Local binary patterns (LBP) feature. These views are in 20, 59 and 40 dimensions respectively; 5) HMDB has 6718 samples of 51 categories of actions, which is consisted of Histogram of oriented gradients (HOG) feature and Motion Boundary Histograms (MBH) features as a 2-view dataset. Both views have 1000 dimensions; 6) CUB dataset has 200 different categories of birds and 11788 images in total. Same as (Han et al., 2021), we used first 10 categories in our experiment and GoogleNet (Szegedy et al., 2015) and doc2vec (Le & Mikolov, 2014) to extract the image features and text features to simulate a 2-view dataset. Image view and text view has 1024 and 300 dimensions respectively.
E. Supplementary Insights and Additional Analysis
E.1. Multi-Viey Agreement with Ground Truth (MVAGT)
The MVAGT (Multi-View Agreement with Ground Truth) is novel evaluation metric designed specifically for multi-view classification problems with conflicting views. It assesses the model's performance on the test set by considering the ground truth labels, thus providing a more reliable and realistic measure of the model's 's ability to handle view disagreements. The rationality behind MVAGT lies in its alignment with real-world scenarios, where the majority agreement among multiple views is often considered more reasonable for the final decision. In the presence of view conflicts, a model that can make predictions consistent with the majority of views is deemed more trustworthy and reliable. By evaluating models using MVAGT, examine the reasonableness of the fused decision and assess the model's 's capability to handle view conflicts effectively. Mathematically, MVAGT calculates the accuracy of the model on the test set follows:
MVAGT = M 1=1
Σ
M
V Σ
1( =
V 2
(16)
where M is the total number of test samples, the number of views, Df" is the predicted label of the i-th sample from the w-th view, y is the ground truth label of the i-th sample, and 1(.) the indicator function that returns if the condition is satisfied and 0 otherwise.
Table 12. MVAGT test split. The best results highlighted in bold and the second-best results are underlined.
Dataset
Handwritten
101tech
PIE
Scene 5
HMDB
CUB
MGP
81.37=5.73
91.55=0.29
63.20+2.31
52.10%0.41
50,43=0.42
42.50+9.22
ECML
74.08=0.61
91.05=0.27
18.
41.91#0.31
50.95=0.48
48.5845.36
TMNR
86.80#
90.92=0.18
65. 15.1543.68
51.8620.61
50.48=0.47
36.58+6642
CCML
86.78=1.42
88.97=1.09
81.91+1.40
55.23+0.84
51.3440.91
63.6742.61
TMC
81.5826.57
90.27=0.38
51.54+3.00
51.4220.46
50.37=0.45
43.25+14.8
ETMC
98.10=0.17
92.41+0.32
75.1544.13
73.75+0.45
8.45+1.09
91.08+1.06
TF (ours)
88.97=0.61
92.01=0.22
80.59=0.75
60.41
52.47=0.35
54.33+7.54
ETF (ours)
98.53=00.8
94.47=0.12
90.37+0.40
79.18+0.38
71.43+0.32
91.17=0.67
E.2. AUROC for Uncertainty.
The uncertainty score, as illustrated in Proposition 3.6, will be more accurate withou introducing biases, so essential to validate the increased uncertainty. Following the approach of prior work (Filos et al., 2019), we assess uncertainty to ensure thorough evaluation. Specifically, we employed AUROC measure the model's s discriminate power in distinguishing incorrect predictions using uncertainty scores. As shown in Table 13, TF and ETF consistently demonstrate the best performance on five out of the six datasets, showcasing their robust generalizability. Despite a performance decrease on the CUB dataset, our method (ETF) still maintains the second-best result, outperforming other approaches, whether incorporating pseudo views or not. One possible reason for the decreased performance on CUB could be the unstable optimization caused by the limited number of training instances (c.g., 480), whereas other datasets, such as Scene15, contain significantly more instances (e.g.. 3588).
E.3. A blation Study of Warm-up Epochs
In the proposed stage-wise training algorithm, we adopt a warm-up stage (i.e., training stage 1) for better initialization of referral networks. As random initialized parameters may not able to assess the reliability of corresponding functional
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Navigating Conflicting Views: Harnessing Trust For Learning
Table 13. AUROC of uncertainty scores for identifying incorrect predictions. The best results highlighted in bold and the second-best results are underlined.
Dataset
Handwritten
Caltech101
PIE
Scene15
HMDB
CUB
MGP
99.29=0.30
87.62=0.90
88.4340.67
63.92+1 196
82.87=0.60
58.20=11.4
ECMI
79.05=5.62
86.31=0.50
87.51=0.49
60.50=0.25
81.63=0.15
57.30+8.50
TMNR
99.42=0.16
87.22=0.57
91.30=1.12
62.39=0.52
82.11=0.41
57.84+3.84
CCML
97.29=0.76
85.87=0.89
86.9841.06
62.5740.52
82.53+0.82
64.2944.35
TMC
99.23=0.22
87.33=0.47
90.1640.99
02.60=0.54
82.63=0.48
63. 13.8
ETMC
99.30=0.19
88.35=0.63
93.02+1 1.40
66,4920.44
85.42=0.34
72.56+8.11
TF (ours)
99.32=0.35
88.99=0.54
95.90=0.08
64.56+2.02
83.59=0.23
53,52+14.3
ETF (ours)
99.90+0.30
88.70=0.54
92.47+1.19
70.44+1.10
86.23+0.49
64.4143.54
1.000
0.9525
0.945
0.9520
accuracy
accuracy
accuracy
0.998
0.910
0.9515
0.997
0.935
0,9510
0.996
0.9505
0.930
0
1
2
20
0
2
5
10
warm-up epochs
warm-up epochs
warm-up epochs
Handwritten
Caltech101
PIE
0.82
0.942
0.7550
0.940
0.81
acccracy
0.7525
accurety
0.938
0.80
0,7500
0.9.0
0.79
0.78
0.932
2
10
10
10
werm-up epechs
warm-up epochs
warm-up epochs
Scenel5
HMDB
CUB
ETF
TF
Figure 3. The effect of different warm-up up epochs on testing accuracy.
opinions correctly. The key hyper- parameter of the warm-up up stage, is the warm-up epochs. We ablate different values of this hypee-parameter and evaluate the effect of it on the performance of our method.
Specially, we used an empirical value, i.., one single epochs, for all reported results in the experiment section. And here we provide more analysis with finely grain values, starting from 0 and increasing steadily, for example, to 2, 5, and 10, that first random initializing the parameters of the referral networks and then not warm-up training or training with 2, 5, 10, and followed by each, finish the rest training stages. Please note that if this value is set to be 0, which means disable the warm-up stage, and reported results with warm-up epoch are also included, as shown in Figure 3.
From Figure 3, we can find that incorporating warm-up stage (warm-up epochs 2 1) can generally results in better accuracy. For some datasets (e.g. HMDB), increasing the number of warm-up epochs further improves accuracy compared to the results previously reported. This observation suggests that adjusting this value based on the specific dataset can lead to enhanced performance.
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Navigating Conflicting Views: Harnessing Trust For Learning
Table 14. View-Specific Pairwise Feature Similarity For Six Datasets
View
Mean
Median
Min
Max
0.6268
0.6329
0.1249
1.0000
2
0.8043
0.8095
0.4456
1.0000
3
0,8586
0,8592
0.6304
1.0000
Handwritten
4
0.7917
0.8038
0.2970
1.0000
5
0.9167
0,9168
0.8137
1.0000
6
0.7036
0.7964
0.0097
1.0000
AVG
0.7836
0.7889
0,5350
1.0000
1
0,9684
0.9725
0.6968
1.0000
10
2
0.9748
0,9792
0,5175
1.0000
AVG
0.9716
0.9756
0.6263
1.0000
1
0.7518
0.7696
0.2842
0.9954
PIE
2
0.7173
0.7203
0.4939
0,8530
3
0.8613
0.8682
0.5598
0,9895
AVG
0.7768
0.7829
0.5471
0.9395
1
0.9038
0.9234
0.0538
1.0000
Scene15
2
0.8689
0.8904
0.1185
1.0000
3
0.8133
0.8385
0.0072
1.0000
AVG
0.8620
0.8789
0.1170
1.0000
1
0,9372
0.9375
0.9002
1.0000
HMDB
2
0.9418
0.9418
0.8898
1.0000
AVG
0.9395
0.9397
0.8970
1.0000
1
0.4112
0.3952
0.1346
0.9577
CUB
2
0,9033
0.9128
0.5949
0.9972
AVG
0.6572
0.6494
0.4153
0.9674
E.4. Instance Similarity of Vector Datasets
We also calculated the pair-wise cosine similarities and provided both the results and an analysis accordingly. Specifically, we considered to calculate the instance similarity using pair-wise cosine similarity. Please note the AVG view means calculating instance similarity each view first, then averaging over all views.
Based on the Table above, we can see that for some datasets, like Handwritten and CUB, different views show different statistics indicating the similarity varies significantly in different views. However, for other datasets, like HMDB and 1altech101, 01, the instance similarity among different views are pretty similar.
As we calculated the pairwise similarity using the feature vectors of instances, this similarity also reflects the semantic similarity. Consequently, similar statistics among different views suggest that their classification performance is likely be comparable.
1) For similar views: If one view achieves high accuracy, the other is likely to perform similarly, resulting in both high accuracy and consistency. For example, this is observed in the Caltech 101 dataset (refer to Top-1 1 Accuracy and Fleiss Kappa). If one view performs with low accuracy, the other tends to perform similarly, leading to fused predictions that are consistently low in accuracy across views. An example this can be seen in the HMDB dataset.
2) For dissimilar views: If one view achieves high accuracy while the other produces low-accuracy predictions, this leads higher conflicts. But the accuracy of the fused prediction depends the specific fusion mechanism employed by the method. Examples of this scenario can be observed in the Handwritten and CUB datasets.
E.5. Reduce Confficts by Trust Fusion
We calculate the Conflict Ratio (CR) by normalizing the number of times that the v-th view prediction is different from w-th view, i.e., CR( )
M Σe (0)" * "!"),, where M is total number of test instances, 9." is the predicted label of i-th instance on w-th view, and I is the indicator function that returns if the condition is satisfied and O otherwise. By applying Trust Discounting, both TMC's and ETMC's conflicts between different views are significant reduced. As an example, the CR on Scene 15 is visualized by heatmap, shown in Figure E.5. The colors in the heatmap generated by our method are noticeably more blue (or less red) than those of the baselines, indicating that the conflict ratio has been reduced by our method.
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Navigating Conflicting Views: Harnessing Trust For Learning
0.7
5
0.22
0.46
0.26
0.54
0.6
Prod
0.5
Pred
0.22
0.41
0.13
0.5
0.4
Views
0.46
0.41
0.48
0.63
0.4
0.17
0.57
0.39
0.2
0.1
0.55
0.54
0.59
Pred
GT
Pred
2
3
Views
TMC
TF
0.7
0.22
0.02
0.31
0.02
0.22
0.18
0.46
0.26
0.6
Pred
Prod
0.18
0
0.42
0.2
0.5
0.085
Views
1
0.62
0.55
0.6
0.69
0.6
Views
0.4
0.26
0.2
0.47
0.59
0.25
0.62
0.59
0.69
0.69
0.59
0.2
0.54
0.5
0.63
0.39
0.51
0.1
0.25
GT
Pred
Pred
2
Views
ETMC
ETF
Figure 4. Conflict Ratio Scene15, Four Methods TMC, TF, ETMC, ETF are compared. GT, Pred, 1, 2, 3 and PS are ground ground-truth. prediction, GIST, PHOG, LBP and pseudo view respectively.
E.6. Explanation for the Decrease of AUROC for Uncertainty
We argue the decreased performance of AUROC whether uncertainty can indicate the correctness of predicted label in caused by insufficient training instances. As shown in Table 11, there are less than 550 training instances PIE and CUB datasets, where our methods, ETF and TF, have decreased performance, compared to ETMC and TMC, in which the only difference is the TD module.
Besides, we also investigate a particular testing instance of CUB dataset for the decreased performance on AUROC of uncertainty. As the error case displayed in Figure 5, ETF corrects the error prediction made by ETMC. However, even though the combined prediction is correct after applying trust discounting, the predictive uncertainty is still relatively high. If ETF corrects previously incorrect predictions but assigns them relatively high uncertainty scores (e.g.. 0.4), it may lead to decrease in the AUROC for predictive uncertainty. This is because AUROC evaluates the model's ability to discriminate between correct and incorrect predictions based on uncertainty scores. Correcting predictions while maintaining high uncertainty scores can make more challenging for the model to distinguish between correct and incorrect predictions, resulting in lower AUROC score, even though the accuracy improves.
E.7. Simulating Confticting Predictions with Noisy Instances
We plot the model performance for Evidential MVC methods with various level of noises introduced to inputs in Figure and Figure 7, for methods incorporate pseudo views and not incorporate pseudo views respectively. Our methods consistently outperforms other methods like TMC and ECMI..
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Navigating Conflicting Views: Harnessing Trust For Learning
0.6
0.5
0.4
0.
0.3
0.
01
ETF
0.
Label 0
Label
Label 2
Label 3 (GT)
Label
Labe! 5
Label
Label 7
Label
Labei Uncertainty
Label 0
Label
Label 2
Label (GT)
Label Label 5
Label
Label 7
Label a
Label 9 Uncertainty
Combined View
Pseudo View
Bellef Mass
Maps
0.5
0.3
0.2
0.-
0.1
an
0.9
Label 0
Label
Label 2
Label 3 (GT)
Label
Label 5
Label
Label 7
Label 8
Label
Unc Uncertainty
Label 0
Label
Label 2
Label (GT)
Label Label 5
Label
Label 7
Label a
Laber Uncertainty
Feat
View
Feat View 2
Figure 5. Bar chart for each label's belief mass and predictive uncertainty of testing instance of CUB dataset. GT indicates the ground truth label of the selected instance.
1.000
0.950
0.94
ETMC ETF
ETF
ETF
0.998
0.945
accuracy
accuracy
accuracy
0.90
0.940
0.996
0.88
0.935
0.86
0.930
10
100
1000
10000
10
100
1000
10000
0
10
100
1000 10000
sigma
sigma
sigma
Handwritten
Caltech101
PIE
0.82
ETMC
0.75
ET
0.94
ETMC
0.80
0.92
acturacy
0.78
accuracy
0.70
xcuraay
0.90
0.76
0.65
0.86
0.72
0.60
0.84
0.70
0.82
1000
10000
10000
16000
sigma
sigma
sigma
Scene15
HMDB
CUB
Figure 6. Performance of pseudo-view incorporated Evidential MVC methods on multi-view data with different levels of noise.
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Navigating Conflicting Views: Harnessing Trust For Learning
0.996
ECML
0.953
ECML
0.925
TMC
TMC
TF
0.945
TF
accuracy
0.994
accuracy
accuracy
0.875
0.940
0.992
0.850
0.935
0.825
ECML
0.990
0.930
0.800
IMC
0.988
0.925
0.775
0
10
100
1000
10000
0
10
100
1000
10000
0
10
100
1000 10000
sigma
sigma
sigma
Handwritten
Caltech101
PIE
ECML
ECML
ECML
0.75
TMC
TMC
0.92
TMC
0.72
TF
TF
0.90
TF
accuracy
accuracy
accuracy
0.70
0.88
0.68
0.65
0.84
0.64
0.50
10
100
1000
10000
0
10
100
1000
10000
0
10
100
1000
10000
sigma
sigma
sigma
Scene
HMDB
CUB
Figure 7. Performance of non pscudo-view incorporated Evidential MVC methods on multi-view data with diffcrent levels of noise.
F. Technical Requirement and Execution
F.1 Limitations
One possible limitation of our work is that the warm-up loss is not optimal solution, even though we explored the impact of different warm-up epochs and showed the effectiveness with using warm-up loss. Another possible limitation would be stage-wise training algorithm is time consuming, we leave it to future work for improving its efficiency.
F.2. Execution Time
The proposed instance-wizg wise approach does indeed introduce additional time complexity compared to the baselines, par- ticularly compared to methods like TMC and ETMC that do not incorporate the TF Module but with same Belief Fusion method. However, method does not rely the dependencies between instances for computation. This allows to perform batch-wise calculations during both training and testing, practice widely adopted in most deep learning algorithms, which can enhance efficiency.
From another perspective, we can view the TF stage as an additional layer appended to the existing framework (e.g.. TMC). Let be the input vector with dimension d,, used for the classification task. a K-class classification problem, we obtain K 1-dimensional functional opinion (1 dimension for uncertainty). The weight matrix W of the proposed BiLinear layer will have dimensions dy dx+1 X d2, and the bias vector will have dimension d2. The time complexity for matrix multiplication is O(d,, x dx+1 d2) and the time complexity for bias addition is O(dz). Thus, the overall time complexity is O(d, x dx+ 1 x d2). Given the dataset for classification task, the additional layer exhibits linear time complexity with respect to only the hidden size. Since this hidden size is relatively small and compact to the classification dimension, we argue that the increase in time complexity not substantial as shown in following tables. We report the training and testing time by averaging 10 times running as shown in Tables 15 20.
F.3. Framework and Reproducibility
For experimental results to be reproducible, we will release our official implementation upon the paper's acceptance. Specifically, we used Torch (Paszke et al., 2019) version 1.13.0, built with CUDA 11.7, to implement our codes. The Python environment version 3.8. and the operating system Ubuntu 22.04.4. All Experiments are conducted on single
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Navigating Conflicting Views: Harnessing Trust For Learning
Table 15. Handwritten
Table 16. Caltech101
Method
Train(Scconds)
Test(Seconds)
Method
Train(Seconds)
Test(Seconds)
F-Avg
22.88+0.30
0.040=0.09
F-Avg
78.62+0.95
0.063=0.09
F-Mode
26.26*0036
0.041=0.09
F- P-Modc
94,01+0.87
0.063+0.09
MGP
1..431=1.43
0,428+0.10 10
MGP
2439.60+7.35
3,428+0.13
EMCL
52.63=1.115
0.041
ECML
152.99+5.96
0.064+0. 10
TMC
35.46+0.78
0.042 0.09
TMC
114.77+1.80
0.06640. 10
TF
183,51#1.81
0.043+0.09
TF
463,41=10.65
0.067=0.09
ETMC
62.45=0.95
0.042+0.09
ETMC
153.64+ 1.690
0.066+0.09
ETF
1422244
0.044+0.09
ETF
543.99+24.88
0.067=0.010
Table 17. PIE
Table 18. Scene
Method
Train(Seconds)
SesonSec
Method
Train(Seconds)
Test(Seconds)
F-Avg
0.94
0.033=00009
F-Avg
27.33+0.37
0.039400009
F-Mode
0.0670.227
0.034+0.09
F-Mode
33.77+0.66
0.040*0.00
MGP
123.63+2.38
0.374+0.11
MGP
576.76+1.27
0,420=0.15
ECML
12.92+1.50
0,035#0.09
ECML
63.2440.72
0,040#0.09
TMC
11.39#0.31
0.035#0.09
TMC
73.26+0.53
0.042+0.1 10
TF
41.63+0.68
0.037=0.09
TF
229.05+2.86
0.042+0.09
ETMC
10.36:0.37
0.036+0.09
ETMC
86.81+3.1 11
0,042+0.09 0.0
ETF
50.39#0.71
0.037+0.09
ETF
271.9942.26
0,043+0.09
Table 19. HMDB
Table 20. CUB
Method
Train(Seconds)
Test(Seconds)
Method
Train(Seconds)
Test(Seconds)
F-Avg
38.26=0.65
0.04520.09
F-Avg
3.57+0.29
0.03340009
F-Mode
48.86#0066
0.048+0.09
F-Mode
4.48+0.22 29
0.033+0.00
MGP
654.42+1.35
0.97120.13
MGP
136.7420.70
0.23940.10
ECML
82.32+1.17
0.047+0.09
ECML
8.1740.2
0,036+0.09
TMC
74.6240.65
0.04720.09
TMC
7.66+0.30
0,03440.09
TF
278,99#3.47
0.047=0.09
TF
29.21#0.41
0.035+0.09
EТMC
99. 09.54
0.046*0.09 099
ETMC
13.98+0.38
0.035*00.09
ETF
365.9428.12
0.047=0.09
ETF
37.57+0.56
0,036+0.09
Nvidia RTX 3090 GPU with 24GB of memory.
G. More Discussions
G.1. Comparison to ECML
The differences between ECML and our work can be summarized as follows,
1. Different Conflict Resolving Mechanism: our method uses a trust discounting module to modulate the trust on the functional opinions, while ECML uses loss function to harmonize different views' functional opinions (this already mentioned the related work section).
2. Our method is built upon TMC and ETMC, using the same belief fusion method, which is the Dempher-Shafe rule for combining different views from opinion perspective. However, ECML uses different, evidence averaging based method to fused opinions.
3. To keep the effectiveness of the introduced TD module, proposed stage-wise training algorithm, which is also different from ECML.
G.2. Comparison to Original Trust Discounting
1. While the original subjective logic framework proposes a global trust-discounting mechanism, we extend it to operate in an instance- wise manner. This critical advancement enables adaptive handling of varying conflict patterns across
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Navigating Conflicting Views: Harnessing Trust For Learning
different samples, significantly enhancing the framework's flexibility.
2. We introduce a novel stage-wise training approach that is essential for keeping the effectiveness of our Trust-Discouning (TD) module. Based on our study, simply incorporating the TD module into existing training frameworks (c.g., ETMC's approach) would actually degrade performance. Our designed training strategy ensures stable optimization and reliable conflict handling.
G.3. Potential Limitations
We analyze potential limitations from both global and local perspectives.
1. From global perspective, the evidential multi-view classification framework relies on late fusion. This means that there is no early interaction between views during feature extraction. Although effective in many cases, this design might limit knowledge integration when different views provide complementary but partial information about the complete pattern.
2. From a local perspective, our Trust Discounting (TD) module has a potential limitation: may still use referral opinions with high uncertainty. Since high uncertainty suggests lower reliability, this indicates that our current trust adjustment mechanism could benefit from more fine- aineigrieed handling of such cases. We acknowledge this as area for future improvement.
25


Reviews:
[{"review_id": "fNFozoTJNy", "reviewer": "Reviewer_KHzU", "summary": "The paper introduces a novel, trust-based discounting method to enhance the existing Evidential Multi-view Framework in the real-world scenario when the various views are not fully aligned on the labels of some of the examples. The authors leverage a belief-fusion process that considers the reliability of the predictions made by individual views via an instance-wise probability-sensitive trust discounting mechanism. The paper also introduces Multi-View Agreement with Ground Truth, which is a novel metric for measuring the reliability of the predictions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your feedback and below is our response to your questions,\n\n1) Regarding AUROC usage:\nWe clarify that our use of AUROC differs fundamentally from TMC [Han et al., 2022]. \nWhile TMC employed AUROC to measure label prediction accuracy, \nwe follow the established uncertainty evaluation framework of [Filos et al., 2019], \nwhere AUROC assesses uncertainty calibration - specifically, \nwhether the model's confidence in its predictions correlates with their correctness.\nIn other words, it measures the rate of referring the least confident predictions\nto human experts.\n\n    This distinction is important because:\n    - As noted in TMC's results (Table 1), AUROC for label prediction consistently shows high scores ($>$95\\%) even when accuracy is modest (67.74\\% on Scene15, 65.26\\% on HMDB), suggesting limited discriminative value\n    - Subsequent works (ECML, CCML, TMNR) have consequently abandoned this usage\n    - Our application evaluates a fundamentally different capability: the model's ability to identify unreliable predictions through uncertainty scoring.\n\n2) Writing logic:\nWe appreciate the suggestions regarding paper organization. While we will carefully consider these recommendations for future work, we believe the current structure most effectively presents our technical contributions and results.\n\n3) Wording improvements:\nWe thank the reviewer for their thoughtful suggestions on phrasing and will incorporate appropriate refinements while maintaining the paper's technical precision.\n\n4) Regarding the question:\nWe appreciate your close examination of our results. There appears to be a misunderstanding regarding the relationship between Tables 1 and 3, which we would like to clarify:\n   - Table 1 demonstrates the limitation of common belief fusion methods (like BCF [Han, 2022]) where, as we stated in Line 190, \"the fused opinion is expected to be higher than those of each individual views\" - this represents the problem scenario we aim to solve\n   - Table 3 presents results after applying our Trust Discounting mechanism, which addresses the conflict issue. The values shown (0.08 < 0.10 < 0.42 < 0.76) are:\n        a) expected outcomes of our method,\n        b) demonstrate proper handling of conflicts, and \n        c) represent an improvement over the Table 1.", "claims_and_evidence": "The Section 3 of the paper is poorly written and organized, which makes it extremely hard to follow (thus, difficult to evaluate the paper's claims). One way to improve its structure would be to:\n- simplify sub-section 3.1 to the bare minimum of terminology and notation\n- add to sub-section 3.1 the definition of referral and functional opinions, which are used in 3.2 (lines 2013-214) without being introduced (easy to prove by performing a simple search in the doc)\n- turn sub-section 3.2 into an illustrative running example; basically show the complete flow of computations (i.e., for Tables 1, 2, and 3, make it clear which values are given inputs and which ones are computed; for this second category, show exactly how they are computed) not only for this particular example, but for all four possible cases of true positive (i.e., the views agree on its label, and the fusion consolidates that decision), a \"false positive\" (i.e., the current example, for which the naive fusion method decides that it is safe, when -in fact- it is not), a false negative (the opposite of the current example in Table 1, when the naive method wrongly predicts \"unsafe\"), and a true negative. Having all flows and computation for these four scenarios side by side will allow any reader to truly and understand your approach. \n- simplify sub-section 3.3 to a version of Algorithm 1 that can be used to trace what happens with each of the four illustrative example in the new sub-section 3.2", "methods_and_evaluation_criteria": "Please re-organize Section 4 and the appendices in such a way that the new Section 4 matches the very intuitive presentation of Table 1 in [Han et all, 2022]. In the current form of the paper, you split the [Han et al, 2022] table, which, helpfully, includes both accuracy and AUROC, in Table 4 from Section 4 and Table 13 in the APPENDIX. \n\nVery confusingly, your Table 13 in appendix D.2 has significantly lower AUROC values for ETMC. The values in Table 1 of [Han et al, 2022] are much higher (actually quite competitive with yours: on the 6 evaluation domains, the Han paper has AUROCs superior (at times far superior) to TF's and ETF's:\n- ETMC:  99.95%,   99.89%,  99.77%,  96.17%,  95.58%, and  99.13%\n- TF:        99.32%,  88.99%,  95.90%,   64.56%, 83.59%,  and 53.52%\n- ETF:      99.90%,  88.70%,  92.47%,  70,44%,  86.23%,  and  64.41%\nThese results should be discussed and fully explained in the main paper", "theoretical_claims": "As all proofs are in the appendices, I did not check them", "experimental_designs_or_analyses": "See comments from \"Methods And Evaluation Criteria\" above", "relation_to_broader_scientific_literature": "The authors seemed to have done an adequate coverage on the broader literature. However, the discrepancy between the results reported in [Han et al, 2022] and those in appendix D.2 are a source of concern.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Please explain the discrepancy between (1) ETF's clear superiority wrt ETMC in Table 5 (Fleiss' Kappa), and (2) a version of your Table 13 that is aligned/reconciled with the results in Table 1 from [Han et al, 2022]. If ETMC truly outperforms your approaches on AUROC, wouldn't this also raise concerns about the usefulness of Fleiss' Kappa in this context.", "other_comments_or_suggestions": "- lines 60-63, left coliumn: please add a reference for the Evidentiary Multi-view framework\n- line 57, right column: the \"can\" in \"our method can also enhance ...\" makes the statement weak; to strengthen the 3rd claim, you may want to replace it by \"does\"\n- line 190: please avoid unnecessary negatives, such as \"is expected to be NOT lower\" --> \"is expected to be higher\"", "questions_for_authors": "- in spite of your claim in line 190 wrt Table 1 (to paraphrase - \"the fused opinion is expected to be higher that those of each individual views\"), in the final results in Table 3 we still have 0.08 < 0.10 < 0.42 < 0.76. Why is this the case?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "n/a"}}, {"review_id": "dmYZWMviEp", "reviewer": "Reviewer_8ky8", "summary": "This paper focuses on conflicting multi-view tasks and identifies that misleading predictions with high confidence (low uncertainty) from specific perspectives are key factors in the errors of conflicting multi-view decision-making. To address this issue, the authors propose a view fusion method based on computational trust. This method draws on the principle of trust discounting from Subjective Logic, assigns a binary opinion to each perspective to obtain the trust level for that perspective, and then uses the trust level to weight and fuse the opinions from all perspectives. Extensive experimental results on datasets show that TF has promising results.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer's recognition of our work's strengths:\n1) the clear organization and presentation of our manuscript, 2) the comprehensive literature review demonstrating deep domain understanding 3) the methodological innovation in our trust-based fusion approach, and 4) the extensive experimental validation demonstrating effectiveness.\nWe have carefully addressed each of the concerns raised and provide detailed responses below. We hope these clarifications fully resolve any remaining questions about our work.\n\n**Weaknesses:**\n\n1) Thank you for raising this important point. A similar concern regarding Experimental Designs or Analyses has been addressed in detail in our response to Reviewer ngw9 (please refer to our first response statement to Reviewer ngw9).\n\n2) Regarding network architecture, we maintain consistency with established approaches (TMC, ETMC, ECML, etc.) for fair comparison:\n    - For the six vector-based datasets, we use identical architectures: a single-layer network with Softplus activation to generate functional opinions.\n    - For the Food101 dataset, we employ standard pre-trained models (ResNet50 for images, BERT-base-uncased for text) as feature encoders (as noted in Section 4.4), and referral and functional networks share these encoders on each view. However, each ENN maintains its own output layer (linear + Softplus) to ensure specialized processing while leveraging common feature representations.\n\n3) Please refer to our response to Reviewer icFc, we will include the limitation our work in the revision.\n\n**Other Comments Or Suggestions:**\n\nWe thank you for pointing this out. We believe that clarifying our innovation will make it clearer.\n1) While the original subjective logic framework proposes a global trust-discounting mechanism, we extend it to operate in an instance-wise manner. This critical advancement enables adaptive handling of varying conflict patterns across different samples, significantly enhancing the framework's flexibility.\n2) We introduce a novel stage-wise training approach that is essential for keeping the effectiveness of our Trust-Discounting (TD) module. Based on our study, simply incorporating the TD module into existing training frameworks (e.g., ETMC's approach) would actually degrade performance. Our designed training strategy ensures stable optimization and reliable conflict handling.\n\n**Questions:**\n\nWe acknowledge that the learned weight p reflects the inherent patterns in the training data. Specifically, when certain views exhibit consistent conflicts, the framework will assign them lower weights.\nHowever, our design is fundamentally instance-wise, meaning it remains effective even when conflicts lack clear trends across the dataset. For example:\n1) in our controlled experiments (Appendix D.7, Fig. 6–7), we artificially introduced random conflicts by corrupting 50\\% of views with noise—a scenario without view-specific bias.\n2) under these conditions, such as our method (ETF) still outperformed baselines like ETMC, demonstrating its robustness to sporadic conflicts.\nThis validates that the framework adapts not only to systematic conflicts but also to instance-specific conflicts. We appreciate the opportunity to clarify this point.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "This paper does not present a theoretical claim or proof. However, through well-designed experiments, it effectively demonstrates the superior performance of the proposed method in addressing conflicting multi-perspective issues compared to existing approaches.", "experimental_designs_or_analyses": "These experiments used six datasets, but only utilized normal datasets, lacking a comparison with the results on datasets with added conflicts.", "relation_to_broader_scientific_literature": "This paper introduces a multi-view fusion method based on computational trust. The proposed trust discounting framework is highly consistent with trustworthy multi-view learning and will play a promoting role in the research on multi-view decision-level fusion, reliable uncertainty estimation, and conflicting multi-view tasks.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Paper strength: \n1.The manuscript is well-organized and clearly written, making complex concepts accessible to a broad audience. The visual representations are well-designed and effectively illustrate the proposed method's motivation, methodological designs, and effectiveness of the proposed method.\n2.A deep understanding of TF Enhanced Evidential MVC is demonstrated through a comprehensive literature review. The fusion formula of TF(TD + BCF) is derived in detail, which proves that the TF method maximizes the belief quality of the ground truth label, and that the TF fusion gains a larger u than the non-discount fusion.\n3.The proposed method is reasonable and innovative. It uses the Trust Discount (TD) in Subjective Logic to assign a binomial opinion to each perspective to measure the trust level, guides the fusion process to generate reliable fused opinions, and solves the wrong predictions caused by opinion conflicts from different perspectives.\n4.Extensive experiments on multiple benchmark datasets show significant improvements of the proposed TF method. The effectiveness of the TD module is validated through ablation studies, the capability of handling view conflicts is verified by evaluating multiple models with MVAGT, and the CR demonstrates the TF method's effectiveness in reducing view prediction conflicts.\nPaper weakness:\n1.Some experimental results need to be added. The paper does not seem to compare the performance after adding conflicts to the datasets. The authors should compare the performance changes of each method on normal datasets and datasets with added conflicts.\n2.The detailed description of the network structure is lacking, which is important for reproducing and thoroughly understanding the paper.\n3.The paper could be strengthened by providing a more detailed analysis of the limitations of the proposed approach. This factor could be significant for guiding future research and practical applications of the methodology.", "other_comments_or_suggestions": "We noticed that your main innovation lies in the trust discount fusion from Subjective Logic, which is a reasonable theoretical choice. However, to more clearly demonstrate the unique value of your research, we hope you can further elaborate on the differences between your innovation and the content of the book.", "questions_for_authors": "This paper introduces the Trust Fusion Enhanced Evidential MVC method based on the Trust Discount in Subjective Logic, and conducts extensive evaluations. Regarding the implementation of the Referral Network, in the context of conflicting multi-view learning, \"conflict\" occurs at random view of each sample, rather than referring to an overall conflict trend in the entire dataset (for example, most samples have a high probability of conflict between view v1 and view v2). After the Referral Network is trained using the training set, when it receives a test sample, it assigns a Binomial opinion to each view of the test sample, thereby assigning a view weight p. The question is whether this weight reflects the overall conflict trend learned from the training set, rather than directly addressing the random inter-views conflict of the current test sample.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "N81klf24ZW", "reviewer": "Reviewer_icFc", "summary": "The paper addresses the issue of conflicting predictions in multi-view classification tasks, where traditional methods often assume views are equally reliable and aligned. It proposes a computational trust-based discounting mechanism within the Evidential Multi-view Classification (MVC) framework. This mechanism employs instance-wise probability-sensitive trust evaluation based on subjective logic, discounting less reliable view predictions before fusion. The method includes a stage-wise training algorithm and demonstrates improved accuracy and consistency across multiple real-world datasets, outperforming existing MVC approaches.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer's valuable feedback and their recognition of:\nour research motivation,\nthe novelty of proposed trust-based discounting mechanism and effectiveness of training algorithm.\nWe have carefully addressed each of the concerns raised with detailed responses below, and we hope these clarifications satisfactorily answer all questions.\n\n**Questions For Authors:**\n\n(1) We adopt the terminology from the Subjective Logic book, \ndistinguishing between referral opinions and functional opinions generated by different types of Evidential Neural Networks (ENNs).\nSpecifically, i) Referral opinions serve as reliability assessments, indicating whether a prediction should be trusted,\nii) Functional opinions directly support decision-making based on their own evidence.\nWe confirm your observation that these are generated by two distinct sets of evidence network parameters,\nand there are still some key differences worthy of highlighting.\n\n- As noted in line 266 and line 270, the referral ENN and functional ENN process different input features, referral ENN takes one more input which is the belief masses of functional opinion;\n- Their roles are also different, as in Eq. 5 (Trust Discounting Mechanism) – referral opinions modulate trust, while functional opinions provide evidence for decision making;\n- Referral opinions are always derived from beta distributions (Fig. 2), quantifying the reliability of their associated functional opinions. However, functional opinions may follow either: 1) Dirichlet distributions for multiclass classification or 2) Beta distributions for binary classification.\n\n\n(2) We appreciate this important question. Let us analyze potential limitations from both global and local perspectives.\n\n- From a global perspective, the evidential multi-view classification framework relies on late fusion. This means that there is no early interaction between views during feature extraction. Although effective in many cases, this design might limit knowledge integration when different views provide complementary but partial information about the complete pattern.\n- From a local perspective, our Trust Discounting (TD) module has a potential limitation: it may still use referral opinions with high uncertainty. Since high uncertainty suggests lower reliability, this indicates that our current trust adjustment mechanism could benefit from more fine-grained handling of such cases. We acknowledge this as an area for future improvement.\n\n**Weaknesses:**\n\n(1) The difference between ECML and our work can be summarized as follows,\n- Different Conflict Resolving Mechanism: our method uses a trust discounting module to modulate the trust on the functional opinions, while ECML uses a loss function to harmonize different views' functional opinions (this is already mentioned in the related work section).\n- Our method is built upon TMC and ETMC, so using the same belief fusion method, which is the Dempher-Shafer rule for combining different views from opinion perspective. However, ECML uses a different, evidence averaging based method to fused opinions.\n- To keep the effectiveness of the introduced TD module, we proposed stage-wise training algorithm, which is also different from ECML.\n\n(2) Thank you for raising this important point. A similar concern regarding Experimental Designs or Analyses has been addressed in detail in our response to Reviewer ngw9 (please refer to our first response statement to Reviewer ngw9).\n\n(3) Please refer to our reply to your same question above\n\n(4) Thank you for raising this important point.\nOur method consistently outperforms existing baselines across all six evaluated datasets. We acknowledge that in some cases, the improvements might appear marginal, which we attribute primarily to the following reasons:\n- Limited improvement space: For datasets such as Handwritten, all compared methods already achieve accuracy levels above 99\\%, leaving minimal room for further substantial gains.\n- Intrinsic difficulty of certain datasets: For challenging datasets like Caltech, existing state-of-the-art methods typically achieve around 94\\% accuracy. Despite this intrinsic difficulty, our method successfully pushes performance beyond this threshold into the 95\\% range, demonstrating effectiveness even under constrained conditions.\n- Baseline instability and fluctuations: On datasets such as CUB, baseline methods exhibit performance instability, fluctuating notably between 90\\% and 93\\% accuracy. In contrast, our approach demonstrates clear superiority by consistently achieving around 94\\% accuracy, indicating greater robustness and stability.\n\nAdditionally, it's important to highlight that on datasets like Scene, our method achieves a substantial improvement of roughly 4-5\\%, while even in the least improved scenario (PIE and HMDB dataset), we still observe a meaningful increase of above 1%.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Empirical experiments conducted on six benchmark datasets convincingly demonstrate that the proposed approach outperforms established baselines in terms of accuracy and consistency metrics, underscoring its practical effectiveness and broad applicability.", "relation_to_broader_scientific_literature": "This paper introduces a novel trust-based mechanism for resolving conflicting information across multiple views in classification tasks. The proposed framework aligns closely with broader efforts in uncertainty estimation and trustworthy multi-view learning, potentially influencing further studies in multi-modal fusion, robustness in decision-making models, and uncertainty-aware ML systems.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "# Strengths\n\n* The paper effectively addresses the practical and often overlooked issue of conflicting predictions in multi-view classification, highlighting scenarios where the standard assumption of equally reliable views does not hold, such as autonomous driving and medical diagnostics.\n\n* A notable strength is the introduction of an innovative trust-based discounting mechanism grounded in subjective logic, which quantifies uncertainty and reliability on an instance-wise basis. This approach uniquely adjusts the fusion process by discounting less reliable view predictions, thus enhancing robustness.\n\n* The authors make a methodological advancement by developing a structured stage-wise training strategy, effectively integrating both functional predictions and referral opinions. This training scheme contributes to improved model robustness, particularly in handling conflicts among views.\n\n# Weaknesses\n\n* While the paper introduces an incremental innovation through a trust-based discounting mechanism, it lacks a clear articulation of how this method differs from and outperforms closely related approaches, such as ECML. Clarifying these distinctions would strengthen the paper's theoretical contribution and impact.\n\n* The paper does not clearly explain how the conflict data is constructed for experiments. The dataset used are all normal multi-view data, which seems contradictory to the conflict scenario the paper aims to address.\n\n* The paper could benefit from a more detailed discussion on scenarios where the proposed method may not perform as expected. Understanding the limitations and failure cases can provide more nuanced insights and guide practical implementations.\n\n* The improvement of the proposed method in some experimental results isn't obvious compared with some other state-of-the-art MVC baselines.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "* What are the definitions of \"referral opinion\" and \"functional opinion\"? Is there any specific difference between their meanings? According to my understanding, they are generated by two separate sets of evidence network parameters.\n\n* Are there known limitations or potential failure cases of the framework that were not discussed in the paper? Understanding where the model might not perform as expected could be crucial for practical implementations.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "N/A"}}, {"review_id": "ZjGvsfmDWZ", "reviewer": "Reviewer_ngw9", "summary": "This paper introduces a novel approach to resolving conflicting predictions in multi-view classification by integrating an instance-wise, probability-sensitive trust discounting mechanism within an evidential framework. The method computes a degree of trust for each view through a referral network, which is then used to discount the functional predictions on a per-instance basis before fusing them using BCF. A stage-wise training strategy is also proposed to alternately optimize the functional and referral networks, leading to improved prediction accuracy and consistency across various datasets, particularly in scenarios where different views provide contradictory information.\n\n## update after rebuttal\n\nThe authors have addressed several of my concerns, and I appreciate the detailed discussion and experimental analyses provided in their rebuttal. I decided to maintain my rating.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We would like to thank you for your valuable feedback,\nand we appreciate your recognition of our novel idea, the effectiveness of the proposed trust discounting method and stage-wise training algorithm, and its adaptivity to the existing evidential framework.\nWe have carefully considered each of the concerns raised and have provided detailed responses below. \n\n**Experimental Designs Or Analyses:**\n\n(1) Our work primarily focuses on prediction conflicts, for example, the case shown in Fig. 1, where different views' prediction is different from others.\nWe agree with you that explicit evaluation on datasets engineered to induce view conflicts is critical. However, existing literature lacks an ideal method for conflict simulation.\nFor example, the only existing work, ECML, attempted to address the conflict issue,\nwhich randomly replaces original features with instances from other classes (e.g., substituting a \"cat\" with a \"dog\")\nto simulate the conflict issue.\nHowever, this approach has limitations:\ni). unrealistic conflicts: random replacements (e.g., swapping \"cat\" with \"airplane\") may create semantically implausible disagreements, \nunlike naturally occurring multi-view conflicts, the one we show in Fig.1.\nii). distribution mismatch: artificially generated conflicts ignore view-specific feature distributions, potentially biasing evaluation results.\n\nInstead, we propose an alternative conflict simulation method (acknowledging its imperfections, we move detailed results to Appendix D.7, Fig. 6–7 due to the space limitation).\nOur approach injects Gaussian noise into a randomly selected half of the views.\nThe intuition is that as noise increases, corrupted views become less informative, approaching random guesses, while uncorrupted views retain (ideally) correct predictions, creating a controlled conflict scenario.\nWe train models with noise injected data, ensuring the conflicts arise during the learning phase.\nAs shown in Fig. 6–7, our methods (ETF/TF) outperform baselines in handling such conflicts.\n\n(2) We agree that direct conflict measurement is important, and we regret that space limitations prevent us from including these results in the main text.\nIn Appendix D.5 and Fig. 4, we measure pairwise view (or inter-view) prediction conflicts and also provide a concrete instance-level example of such conflicts in Fig. 5.\nOur methods TF and ETF builds upon TMC and ETMC, and as shown in Fig. 4, both our methods consistently and significantly reduce conflict ratios across all view pairs.\nFor example, the prediction conflict ratio between the GIST view and the pseudo view of EMTC is 0.6, and\nour method ETF reduces it to 0.45, a 15\\% absolute improvement.\n\nFor the instance level case illustrated in Fig. 5, the original ETMC prediction was Label 4 (while the ground truth was Label 3). After applying ETF, the model correctly shifts higher belief mass to the true label (Label 3).\nNotably, this improvement is not limited to the combined view, it also enhances consistency in view-specific predictions (e.g., pseudo-view and View 2), demonstrating stronger agreement among views.\n\n**Essential References Not Discussed:**\n\nWe appreciate the reviewer for highlighting the newly accepted ICLR 2025 work, which we recognize as concurrent research to ours and worthy of discussion in our submission. However, after thorough investigation, we were only able to identify one relevant paper titled ``Trusted Multi-View Classification via Evolutionary Multi-View Fusion\". While the authors provide a GitHub link, we note the implementation appears unavailable at this time.\n\nUpon careful analysis, we find their work orthogonal to ours: their primary contribution focuses on enhancing pseudo-view quality through evolutionary fusion, whereas we propose a fundamentally different approach for handling prediction conflict via Trust Discounting.\nThat said, we acknowledge potential synergies - our ETF method could potentially incorporate their evolutionary architecture by: i) integrating our referral ENN to further refine the pseudo-view generated by their evolutionary fusion,\nand ii) maintaining our core innovation in conflict resolution while benefiting from their improved pseudo-view feature.\nThis complementary combination might yield additional improvements, though we leave such exploration for future work given the current unavailability of their implementation.\n\nIn the revised manuscript, we will explicitly cite and discuss the recommended paper, highlighting its relevance and clarifying how our work differs from or builds upon it.\n\nWe hope our responses effectively address your concerns and appreciate if you can reconsider and improve the evaluation.", "claims_and_evidence": "Yes. I think the claims are supported.", "methods_and_evaluation_criteria": "The proposed methods are well-motivated and generally appropriate for addressing conflicts in multi-view classification. The trust discounting mechanism, combined with the stage-wise training strategy, effectively integrates into the evidential framework, enhancing both prediction accuracy and consistency. However, the evaluation primarily relies on standard classification benchmarks (e.g., Handwritten, Caltech101, PIE, etc.) and metrics like Top-1 Accuracy and Fleiss’ Kappa. While these are valid, they might not fully capture the nuanced challenge of handling view conflicts. Additional evaluation criteria—such as direct conflict quantification metrics or experiments on synthetic datasets with induced conflicts—could provide a more comprehensive validation of the method for the intended application.", "theoretical_claims": "I examined the proofs provided for several theoretical claims, particularly focusing on Proposition 3.5 and Proposition 3.6 (detailed in Appendix B.4). And the derivations are consistent with the framework of subjective logic and evidential deep learning, and I did not identify any glaring mathematical errors.", "experimental_designs_or_analyses": "Q1. The benchmarks used are standard classification datasets that may not naturally exhibit strong conflicting views. Additional experiments on synthetic or real-world datasets specifically engineered to induce view conflicts could provide more direct validation of the method's core motivation.\n\nQ2. The analysis primarily emphasizes classification accuracy and consistency, but further evaluation—such as direct measures of conflict (e.g., disagreement ratios between views) or error analysis on instances with high inter-view disagreement—would strengthen the conclusions regarding conflict resolution.", "relation_to_broader_scientific_literature": "The method extends evidential deep learning techniques by incorporating trust discounting to adjust the influence of unreliable views. It also advances previous multi-view classification frameworks such as Trusted Multi-View Classification (TMC) (Han et al., 2021) and its variants (e.g., ETMC, ECML, TMNR, CCML) by explicitly modeling and mitigating the impact of conflicts among views.", "essential_references_not_discussed": "No additional essential references appear to be missing. However, it is recommended that the authors discuss two newly accepted ICLR’25 papers related to Trusted Multi-View Classification to further contextualize and strengthen the contributions of this work.", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "- Line 355, the right column, ‘our methods’\n- Lin 318, the left column, ‘since the’", "questions_for_authors": "Please see the above questions. If the authors can adequately address these concerns, I would be happy to reconsider and improve my evaluation of the paper.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper considers the multi-view classification issue under the scenarios where views are of unequal importance and are not fully aligned. The authors design a computational trust-based discounting method to improve current evidential multi-view framework under view conflicts, and utilize an instance-wise probability-sensitive trust discounting mechanism to increase the reliability of predictions made by individual views. Experimental results illustrate the effectiveness. 


After rebuttal, three reviewers give 'Weak accept', and one reviewer gives 'Accept'.  Reviewer KHzU holds that current draft is not a nice and clean version. Reviewer icFc hods that the response addresses several of his/her concerns. Combined with the response and reviewers' feedback, I recommend a 'Weak accept'.
Decision: Accept (poster)