Title: Improving Evolutionary Multi-View Classification via Eliminating Individual Fitness Bias
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-View Classification; Evolutionary Multi-View Classification;  Individual Fitness Bias
Abstract: Evolutionary multi-view classification (EMVC) methods have gained wide recognition due to their adaptive mechanisms. Fitness evaluation (FE), which aims to calculate the classification performance of each individual in the population and provide reliable performance ranking for subsequent operations, is a core step in such methods. Its accuracy directly determines the correctness of the evolutionary direction. That is, when FE fails to correctly reflect the superiority-inferiority relationship among individuals, it will lead to confusion in individual performance ranking, which in turn misleads the evolutionary direction and results in trapping into local optima. This paper is the first to identify the aforementioned issue in the field of EMVC and call it as fitness evaluation bias (FEB). FEB may be caused by a variety of factors, and this paper approaches the issue from the perspective of view information content: existing methods generally adopt joint training strategies, which restrict the exploration of key information in views with low information content. This makes it difficult for multi-view model (MVM) to achieve optimal performance during convergence, which in turn leads to FE failing to accurately reflect individual performance rankings and ultimately triggering FEB. To address this issue, we propose an evolutionary multi-view classification via eliminating individual fitness bias (EFB-EMVC) method, which alleviates the FEB issue by introducing evolutionary navigators for each MVM, thereby providing more accurate individual ranking. Experimental results fully verify the effectiveness of the proposed method in alleviating the FEB problem, and the EMVC method equipped with this strategy exhibits more superior performance compared with the original EMVC method. (The code is available at https://github.com/LiShuailzn/Neurips-2025-EFB-EMVC)


Peer reviews:
comments: SUMMARY:
The paper proposes a novel approach for evolutionary multi-view classification (EMVC). It highlights that existing EMVC methods suffer from fitness evaluation bias, primarily due to significant differences in the information content among views, resulting in the model not to achieve optimal performance when converging. To address this issue, the authors introduce evolutionary navigators for each multi-view for calibrating the evolutionary direction in real time. This approach demonstrates superior performance across nine multi-view datasets.

STRENGTHS AND WEAKNESSES:
***Strengths***

(1) The paper presents an important and novel problem—fitness evaluation bias (FEB)—which has not been previously explored in the community of the EMVCs.
(2) The paper is well-structured with logic flow and nice graph illustrations. For instance, Figure 1 clearly differentiates the existing EMVC methods and the proposed EFB-EMVC.
(3) The experiments are well-designed and comprehensive, comparing the EFB-EMVC with four kinds of strong SOTA baselines on popular benchmark datasets. Analysis and experiment results also provide inspiring viewpoint.

***Weaknesses***

I find this work well-organized and convincing, with no apparent shortcomings. One thing I can suggest is to provide more descriptions for crossover and mutation (Line 202), and the compared baselines (Line 233). Int this version, they are too simple, which brings difficulty to the readers that do not very familiar with this topic.

QUESTIONS:
(1) How do the crossover and mutation operators work? More details are necessary to the readers that do not very familiar with this topic.

(2) How are the hyper-parameters of compared baselines set?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an evolutionary multi-view classification via eliminating individual fitness bias approach (EFB-EMVC). By introducing the evolutionary navigators mechanism, it realizes the migration of rich intra-class and cross-class information from the pre-trained teacher model to the view branches of the multi-view model (MVM), addressing the fitness evaluation bias (FEB) problem that has not been previously concerned in the evolutionary multi-view classification (EMVC) task. Experimental results show that the method proposed in this paper achieves optimal experimental results compared with various state-of-the-art (SOTA) methods. In the ablation experiment section, detailed and reasonable experimental settings and data verification demonstrate that this method effectively solves the FEB problem.\n\nSTRENGTHS AND WEAKNESSES:\nMy comments on the strengths are:\n1. I verified the relevant steps of extracting rich cross-class information from the pre-trained teacher model by constructing a progressive matrix, and implementing knowledge transfer of cross-class information through a Wasserstein distance (WD)-guided loss function. Overall, the logic is reliable.\n2. I reviewed the experimental design and performance report. The overall setup is reasonable.\n3. This study first bridges EMVC and knowledge distillation (KD), overcoming the FEB problem that has not been addressed in the EMVC field before. The novel \"evolutionary navigators\" mechanism achieves effective improvement in KD performance, realizing cutting-edge accuracy in complex and unknown multi-view scenarios.\n4. In numerous complex multi-view datasets, this method achieves performance improvements compared to cutting-edge technologies, demonstrating practical application value.\n\nMy comments on the weaknesses by seeing the questiongs.\n\nQUESTIONS:\n1. In Figure 1 of the paper, the performance curves of View 4 under three different scenarios are presented. Among them, \"V4 only\" refers to the pre-trained teacher model of View 4, and its pre-training method can be clarified according to the content shown in Figure 2 (b). However, there are still questions regarding the determination methods of the performance curves of View 4 in \"V4 in MVM\" and \"V4 with EN\". It is recommended to supplement the relevant details.\n2. Compared to Table 1, Table 2 show that the proposed method has the limited improvement, it is necessary to provide more deep discussion. \n3. Table 3 shows the rank relationship between with-En and without-EN, it is very key for showing the necessary of the individual fitness bias elimination. So, it is suggested to add the detailed formulations of PC, SPC and KT, and give the association criterion.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel knowledge distillation (KD) mechanism called evolutionary navigators (ENs), aiming to address the fitness evaluation bias (FEB) problem in evolutionary multi-view classification (EMVC). The method demonstrates outstanding performance on various datasets. Notably, ENs serve as a flexible plug-in that can be integrated into different approaches to achieve significant performance improvements.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The claims proposed in the paper are supported by clear and persuasive evidence.\n2. The method proposed in the paper and its evaluation criteria are highly compatible with the research questions and application scenarios.\n3. The experimental design and analysis in the paper are highly reasonable and effective. In the ablation experiments, the authors systematically removed or modified different components of EFC-EMVC (such as ENs, KL divergence, and WDK loss function). This design effectively evaluates the impact of each module on overall performance, clearly addressing the question of their individual contributions. \n4. This paper first identifies the FEB problem in EMVC and proposes a highly effective solution from this perspective. The method is expected to promote the development of the EMVC field.\n\nWeaknesses:\n1. In the hyperparameter analysis section of the supplementary materials, the paper analyzes the impact of different view fusion dimensions on performance. However, hyperparameters such as batch size also affect model performance, and I hope to see detailed information on how such hyperparameters influence performance.\n2. There are some missing details. For example, in line 133, the novel distillation loss function proposed by the author is named \"WDK\", and the ablation experiment on the right side of Figure 3 also ablates \"WDK\". However, in Table 5 of the appendix, the distillation loss function is \"WDT\". If this is a typo, it is recommended to rewrite it. If they are two different loss functions, it is suggested to provide more explanations.\n3. Some sentences in the paper have incomplete semantics or unclear expressions. For example, the part about feature matrix construction in lines 117-120. The term \"feature vector\" is somewhat ambiguous. In this context, it is unclear which part of the output result the \"feature vector\" specifically refers to.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper first discovers the \"fitness evaluation bias (FEB) problem\" in the current evolutionary multi-view classification (EMVC). The problem is manifested in that existing EMVC methods all adopt a joint training strategy, leading to insufficient utilization of low-information content view information in the multi-view model (MVM). As a result, the MVM fails to reach its performance limit when converging. Subsequently, the authors propose an evolutionary navigators mechanism based on intra-class and inter-class distillation. This mechanism can fully utilize the information of each view while ensuring the overall training direction of the model. Experimental results show that this method outperforms various state-of-the-art (SOTA) methods on all datasets, and the proposed evolutionary navigators mechanism can be well integrated into various methods to achieve significant performance improvements.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1）\tThe method proposed in the paper effectively reveals and solves the FEB problem in current EMVC (as shown in Figure 1 and Table 3), which will become an important approach to promote the development of this field.\n2）\tThe experiments are very comprehensive, covering various SOTA methods, including but not limited to trustworthy multi-view classification methods, traditional adaptive multi-view classification methods, and EMVC methods, etc. The authors also elaborate in detail the reasons for selecting each category of comparative methods and the suboptimal performance of the corresponding methods. Meanwhile, the authors provide evidence support for the strong adaptability of evolutionary navigators through detailed experimental results (as shown in Table 4).\n3）\tReading this paper has provided me with very valuable insights into the limitations of the EMVC field and the corresponding solutions.\nWeaknesses：\n1）As mentioned in the paper, evolutionary navigators are introduced into the MVM to fully utilize the information of each view and solve the FEB problem. I have reviewed the experimental section and supplementary materials of the paper. Although the authors have conducted relatively comprehensive experiments with broad coverage, they have not reported the accuracy of the pretrained teacher models for each view on the respective datasets. I suggest the authors include this information in the revision to enhance the interpretability and credibility of the experimental results.\n2） Moreover, the paper uses well known public datasets such as MVoxCeleb, YoutubeFace, NUS-WIDE-128, Reuters, CB, MM-IMDB, NTU RGBD, and EgoGesture. These datasets are suitable for the multi-view classification task, but it should be mentioned if any preprocessing was performed, as it may affect the results.\nOverall, although there are some weaknesses in the paper, I am still happy to see that this paper proposes a new task in EMVC, called eliminating individual fitness bias, which might lead to some future works. Besides this, I also think that the proposed individual fitness bias elimination strategy would have a positive impact on the existing EMVCs. Thus, I tend to accept it.\n\nQUESTIONS:\nPlease see Weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper innovatively identifies a key limitation in evolutionary multi-view classification (EMVC): due to significant differences in information content across views, multi-view models (MVMs) using uniform training objectives fail to achieve optimal convergence performance, termed the \"fitness evaluation bias (FEB) problem\". It systematically expounds the impact of this problem on the entire evolutionary process. To address this, the authors propose an evolutionary multi-view classification via eliminating individual fitness bias approach (EFB-EMVC), which uses an evolutionary navigators mechanism to partition knowledge distillation (KD) into intra-class and inter-class distillation for full utilization of information from each view. Experiments on numerous benchmark datasets validate the effectiveness of EFB-EMVC. Meanwhile, evolutionary navigators serve as a flexible plug-and-play module, enabling seamless integration into various methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1）\tThe paper is rich in content and solid in argumentation, with smooth and accessible writing, comprehensive experiments, and in-depth analysis. In particular, Table 3 in the paper shows the performance rankings of 15 individuals before and after applying evolutionary navigators, along with extremely low correlation coefficients. These experimental results fully validate the existence of the FEB problem in the field of EMVC and the effectiveness of the proposed method.\n2）\tThe paper's diagrams are clearly structured. The EMVC process and method framework in Figure 2 accurately convey the authors' intentions, making it easy to understand how each module interacts.\n3）\tI have reviewed the supplementary materials. The paper provides very sufficient details for the experimental part, including the experimental environment, training methods, hyperparameter selection, selected datasets, etc.\n\nWeaknesses:\n1）\tIn Section A.2 of the supplementary materials, I noticed that the proposed method was applied with only 5 iterations of population evolution on the MVoxCeleb dataset, whereas the baseline method TEF was trained with 20 iterations. According to the experimental results in Table 1 of the main paper, the proposed method achieves a 1.94% performance improvement while using only about one-fifth of the iteration budget compared to TEF. This raises an interesting question: if the proposed method were also trained for 20 iterations, would its performance continue to improve significantly, or has it already converged and stabilized around 94.35% after 5 iterations? I suggest the authors further analyze or provide additional evidence to clarify this point, which would help strengthen the validation of their method’s effectiveness.\n\nQUESTIONS:\nIn Section A.2 of the supplementary materials, I noticed that the proposed method was applied with only 5 iterations of population evolution on the MVoxCeleb dataset, whereas the baseline method TEF was trained with 20 iterations. According to the experimental results in Table 1 of the main paper, the proposed method achieves a 1.94% performance improvement while using only about one-fifth of the iteration budget compared to TEF. This raises an interesting question: if the proposed method were also trained for 20 iterations, would its performance continue to improve significantly, or has it already converged and stabilized around 94.35% after 5 iterations? I suggest the authors further analyze or provide additional evidence to clarify this point, which would help strengthen the validation of their method’s effectiveness.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}