waashk/dblp huggingface dataset:
on the integration of neural classifiers through similarity analysis of higher order features. a novel methodology is herein outlined for combining the classification decisions of different neural network classifiers. instead of the usual approach for applying voting schemes on the decisions of their output layer neurons, the proposed methodology integrates higher order features extracted by their upper hidden layer units. more specifically, different instances (cases) of each such classifier, derived from the same training process but with different training parameters, are investigated in terms of their higher order features, through similarity analysis, in order to find out repeated and stable higher order features. then, all such higher order features are integrated through a second stage neural network classifier having as inputs suitable similarity features of them. the herein suggested hierarchical neural system for pattern recognition shows improved classification performance in a computer vision task. the validity of this novel combination approach has been investigated when the first stage neural classifiers involved correspond to different feature extraction methodologies (fem) for shape classification. the experimental study illustrates that such an approach, integrating higher order features through similarity analysis of a committee of the same classifier instances (cases) and a second stage neural classifier, outperforms other combination methods, like voting combination schemes as well as single neural network classifiers having as inputs all fems derived features. in addition, it outperforms hierarchical combination methods non performing integration of cases through similarity analysis.
attribute constrained rules for partially labeled sequence completion. sequential pattern and rule mining have been the focus of much research, however predicting missing sets of elements within a sequence remains a challenge. recent work in survey design suggests that if these missing elements can be inferred with a higher degree of certainty, it could greatly reduce the time burden on survey participants. to address this problem and the more general problem of missing sensor data, we introduce a new form of constrained sequential rules that use attribute presence to better capture rule confidence in sequences with missing data than previous constraint based techniques. specifically we examine the problem of given a partially labeled sequence of sets, how well can the missing attributes be inferred. our study shows this technique significantly improves prediction robustness when even large amounts of data are missing compared to traditional techniques.
self-training strategies for handwriting word recognition. handwriting recognition is an emerging subfield of human-computer interaction that has many potential industrial applications, e.g. in postal automation, bank check processing, and automatic form reading. training a recognizer, however, requires a substantial amount of training examples together with their corresponding ground truth, which needs to be created by humans. a promising way to significantly reduce this effort, and hence cut system development costs, is offered by semi-supervised learning, in which both text with and text without transcription is used for training. however, until today there is no straightforward and established way of semi-supervised learning, particularly not for handwriting recognition. in the self-training approach, an initially trained recognition system creates a new training set from unlabeled data. using this set, a new recognizer is created. the creation of the training set is done by selecting elements from the unlabeled set, according to their recognition confidence. the success of self-training depends crucially on the data selected. in this paper, we test and compare different rules used to select new training data for single word recognition with and without additional language information in the form of a dictionary. we demonstrate that it is possible to substantially increase the recognition accuracy for both systems.
electronic nose ovarian carcinoma diagnosis based on machine learning algorithms. ovarian carcinoma is one of the most deadly diseases, especially in the case of late diagnosis. this paper describes the result of a pilot study on an early detection method that could be inexpensive and simple based on data processing and machine learning algorithms in an electronic nose system. experimental analysis using real ovarian carcinoma samples is presented in this study. the electronic nose used in this pilot test is very much the same as a nose used to detect and identify explosives. however, even if the apparatus used is the same, it is shown that the use of proper algorithms for analysis of the multi-sensor data from the electronic nose yielded surprisingly good results with more than 77% classification rate. these results are suggestive for further extensive experiments and development of the hardware as well as the software.
mining determining sets for partially defined functions. this paper describes an algorithm that determines the minimal sets of variables that determine the values of a discrete partial function. the apriori-like algorithm is based on the dual hereditary property of determining sets. experimental results are provided that demonstrate the efficiency of the algorithm for functions with up to 24 variables. the dependency of the number of minimal determining sets on the size of the specification of the partial function is also examined.
screening paper runnability in a web-offset pressroom by data mining. this paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom. two approaches are explored. the first one treats the problem as a task of data classification into "break" and "non break" classes. the procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. the search process results in a set of input variables providing the lowest average loss incurred in taking decisions. the second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. the tests have shown that the web tension parameters are amongst the most important ones. it was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. the average classification accuracy of the break cases was equal to 76.7%.
so_mad: sensor mining for anomaly detection in railway data. today, many industrial companies must face problems raised by maintenance. in particular, the anomaly detection problem is probably one of the most challenging. in this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. we first address the problem of characterizing normal behavior. in order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). we then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and possible causes of a detected anomaly.
application of classification association rule mining for mammalian mesenchymal stem cell differentiation. in this paper, data mining is used to analyze the differentiation of mammalian mesenchymal stem cells (mscs). a database comprising the key parameters which, we believe, influence the destiny of mammalian mscs has been constructed. this paper introduces classification association rule mining (carm) as a data mining technique in the domain of tissue engineering and initiates a new promising research field. the experimental results show that the proposed approach performs well with respect to the accuracy of (classification) prediction. moreover, it was found that some rules mined from the constructed msc database are meaningful and useful.
time series shapelets: a new primitive for data mining. classification of time series has been attracting great interest over the past decade. recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. while this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. first, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in a time and space complexity that limits its applicability, especially on resource-limited sensors. second, beyond mere classification accuracy, we often wish to gain some insight into the data. in this work we introduce a new time series primitive, time series shapelets, which addresses these limitations. informally, shapelets are time series subsequences which are in some sense maximally representative of a class. as we shall show with extensive empirical evaluations in diverse domains, algorithms based on the time series shapelet primitives can be interpretable, more accurate and significantly faster than state-of-the-art classifiers.
on the tradeoff between privacy and utility in data publishing. in data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. in the meanwhile, they reduce the utility of the data. it is important to consider the tradeoff between privacy and utility. in a paper that appeared in kdd 2008, brickell and shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data, and concluded that "even modest privacy gains require almost complete destruction of the data-mining utility". this conclusion seems to undermine existing work on data anonymization. in this paper, we analyze the fundamental characteristics of privacy and utility, and show that it is inappropriate to directly compare privacy with utility. we then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment, and propose an integrated framework for considering privacy-utility tradeoff, borrowing concepts from the modern portfolio theory for financial investment. finally, we evaluate our methodology on the adult dataset from the uci machine learning repository. our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility.
co-clustering on manifolds. co-clustering is based on the duality between data points (e.g. documents) and features (e.g. words), i.e. data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points. in the past decade, several co-clustering algorithms have been proposed and shown to be superior to traditional one-side clustering. however, existing co-clustering algorithms fail to consider the geometric structure in the data, which is essential for clustering data on manifold. to address this problem, in this paper, we propose a dual regularized co-clustering (drcc) method based on semi-nonnegative matrix tri-factorization. we deem that not only the data points, but also the features are sampled from some manifolds, namely data manifold and feature manifold respectively. as a result, we construct two graphs, i.e. data graph and feature graph, to explore the geometric structure of data manifold and feature manifold. then our co-clustering method is formulated as semi-nonnegative matrix tri-factorization with two graph regularizers, requiring that the cluster labels of data points are smooth with respect to the data manifold, while the cluster labels of features are smooth with respect to the feature manifold. we will show that drcc can be solved via alternating minimization, and its convergence is theoretically guaranteed. experiments of clustering on many benchmark data sets demonstrate that the proposed method outperforms many state of the art clustering methods.
a principled and flexible framework for finding alternative clusterings. the aim of data mining is to find novel and actionable insights in data. however, most algorithms typically just find a single (possibly non-novel/actionable) interpretation of the data even though alternatives could exist. the problem of finding an alternative to a given original clustering has received little attention in the literature. current techniques (including our previous work) are unfocused/unrefined in that they broadly attempt to find an alternative clustering but do not specify which properties of the original clustering should or should not be retained. in this work, we explore a principled and flexible framework in order to find alternative clusterings of the data. the approach is principled since it poses a constrained optimization problem, so its exact behavior is understood. it is flexible since the user can formally specify positive and negative feedback based on the existing clustering, which ranges from which clusters to keep (or not) to making a trade-off between alternativeness and clustering quality.
learning optimal ranking with tensor factorization for tag recommendation. tag recommendation is the task of predicting a personalized list of tags for a user given an item. this is important for many websites with tagging capabilities like last.fm or delicious. in this paper, we propose a method for tag recommendation based on tensor factorization (tf). in contrast to other tf methods like higher order singular value decomposition (hosvd), our method rtf ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. rtf handles missing values and learns from pairwise ranking constraints. our optimization criterion for tf is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. in all, rtf directly optimizes for the actual problem using a correct interpretation of the data. we provide a gradient descent algorithm to solve our optimization problem. we also provide an improved learning and prediction method with runtime complexity analysis for rtf. the prediction runtime of rtf is independent of the number of observations and only depends on the factorization dimensions. besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like folkrank, pagerank and hosvd both in quality and prediction runtime.
efficiently learning the accuracy of labeling sources for selective sampling. many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. however, what if there are multiple labeling sources ('oracles' or 'experts') with different but unknown reliabilities? with the recent advent of inexpensive and scalable online annotation tools, such as amazon's mechanical turk, the labeling process has become more vulnerable to noise - and without prior knowledge of the accuracy of each individual labeler. this paper addresses exactly such a challenge: how to jointly learn the accuracy of labeling sources and obtain the most informative labels for the active learning task at hand minimizing total labeling effort. more specifically, we present iethresh (interval estimate threshold) as a strategy to intelligently select the expert(s) with the highest estimated labeling accuracy. iethresh estimates a confidence interval for the reliability of each expert and filters out the one(s) whose estimated upper-bound confidence interval is below a threshold - which jointly optimizes expected accuracy (mean) and need to better estimate the expert's accuracy (variance). our framework is flexible enough to work with a wide range of different noise levels and outperforms baselines such as asking all available experts and random expert selection. in particular, iethresh achieves a given level of accuracy with less than half the queries issued by all-experts labeling and less than a third the queries required by random expert selection on datasets such as the uci mushroom one. the results show that our method naturally balances exploration and exploitation as it gains knowledge of which experts to rely upon, and selects them with increasing frequency.
exploring social tagging graph for web object classification. this paper studies web object classification problem with the novel exploration of social tags. automatically classifying web objects into manageable semantic categories has long been a fundamental preprocess for indexing, browsing, searching, and mining these objects. the explosive growth of heterogeneous web objects, especially non-textual objects such as products, pictures, and videos, has made the problem of web classification increasingly challenging. such objects often suffer from a lack of easy-extractable features with semantic information, interconnections between each other, as well as training examples with category labels. in this paper, we explore the social tagging data to bridge this gap. we cast web object classification problem as an optimization problem on a graph of objects and tags. we then propose an efficient algorithm which not only utilizes social tags as enriched semantic features for the objects, but also infers the categories of unlabeled objects from both homogeneous and heterogeneous labeled objects, through the implicit connection of social tags. experiment results show that the exploration of social tags effectively boosts web object classification. our algorithm significantly outperforms the state-of-the-art of general classification methods.
efficient anomaly monitoring over moving object trajectory streams. lately there exist increasing demands for online abnormality monitoring over trajectory streams, which are obtained from moving object tracking devices. this problem is challenging due to the requirement of high speed data processing within limited space cost. in this paper, we present a novel framework for monitoring anomalies over continuous trajectory streams. first, we illustrate the importance of distance-based anomaly monitoring over moving object trajectories. then, we utilize the local continuity characteristics of trajectories to build local clusters upon trajectory streams and monitor anomalies via efficient pruning strategies. finally, we propose a piecewise metric index structure to reschedule the joining order of local clusters to further reduce the time cost. our extensive experiments demonstrate the effectiveness and efficiency of our methods.
audience selection for on-line brand advertising: privacy-friendly social network targeting. this paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding good audiences for brand advertising (as opposed to click maximizing, for example). targeting social-network neighbors resonates well with advertisers, and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously. besides being one of the first papers to our knowledge on data mining for on-line brand advertising, this paper makes several important contributions. we introduce a framework for evaluating brand audiences, in analogy to predictive-modeling holdout evaluation. we introduce methods for extracting quasi-social networks from data on visitations to social networking pages, without collecting any information on the identities of the browsers or the content of the social-network pages. we introduce measures of brand proximity in the network, and show that audiences with high brand proximity indeed show substantially higher brand affinity. finally, we provide evidence that the quasi-social network embeds a true social network, which along with results from social theory offers one explanation for the increase in brand affinity of the selected audiences.
doulion: counting triangles in massive graphs with a coin. counting the number of triangles in a graph is a beautiful algorithmic problem which has gained importance over the last years due to its significant role in complex network analysis. metrics frequently computed such as the clustering coefficient and the transitivity ratio involve the execution of a triangle counting algorithm. furthermore, several interesting graph mining applications rely on computing the number of triangles in the graph of interest. in this paper, we focus on the problem of counting triangles in a graph. we propose a practical method, out of which all triangle counting algorithms can potentially benefit. using a straightforward triangle counting algorithm as a black box, we performed 166 experiments on real-world networks and on synthetic datasets as well, where we show that our method works with high accuracy, typically more than 99% and gives significant speedups, resulting in even ≈ 130 times faster performance.
drosophila gene expression pattern annotation using sparse features and term-term interactions. the drosophila gene expression pattern images document the spatial and temporal dynamics of gene expression and they are valuable tools for explicating the gene functions, interaction, and networks during drosophila embryogenesis. to provide text-based pattern searching, the images in the berkeley drosophila genome project (bdgp) study are annotated with ontology terms manually by human curators. we present a systematic approach for automating this task, because the number of images needing text descriptions is now rapidly increasing. we consider both improved feature representation and novel learning formulation to boost the annotation performance. for feature representation, we adapt the bag-of-words scheme commonly used in visual recognition problems so that the image group information in the bdgp study is retained. moreover, images from multiple views can be integrated naturally in this representation. to reduce the quantization error caused by the bag-of-words representation, we propose an improved feature representation scheme based on the sparse learning technique. in the design of learning formulation, we propose a local regularization framework that can incorporate the correlations among terms explicitly. we further show that the resulting optimization problem admits an analytical solution. experimental results show that the representation based on sparse learning outperforms the bag-of-words representation significantly. results also show that incorporation of the term-term correlations improves the annotation performance consistently.