Data Mining and Knowledge Discovery最新文献

{"title":"Time series clustering with random convolutional kernels","authors":"","doi":"10.1007/s10618-024-01018-x","DOIUrl":"https://doi.org/10.1007/s10618-024-01018-x","url":null,"abstract":"<h3>Abstract</h3> <p>Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"8 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140562687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable linear dimensionality reduction based on bias-variance analysis","authors":"","doi":"10.1007/s10618-024-01015-0","DOIUrl":"https://doi.org/10.1007/s10618-024-01015-0","url":null,"abstract":"<h3>Abstract</h3> <p>One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select a small number of the relevant, nonredundant features to preserve the complete information contained in the original dataset, with little collinearity among features. This procedure helps mitigate problems like overfitting and the curse of dimensionality, which arise when dealing with high-dimensional problems. On the other hand, it is not desirable to simply discard some features, since they may still contain information that can be exploited to improve results. Instead, <em>dimensionality reduction</em> techniques are designed to limit the number of features in a dataset by projecting them into a lower dimensional space, possibly considering all the original features. However, the projected features resulting from the application of dimensionality reduction techniques are usually difficult to interpret. In this paper, we seek to design a principled dimensionality reduction approach that maintains the interpretability of the resulting features. Specifically, we propose a bias-variance analysis for linear models and we leverage these theoretical results to design an algorithm, <em>Linear Correlated Features Aggregation</em> (LinCFA), which aggregates groups of continuous features with their average if their correlation is “sufficiently large”. In this way, all features are considered, the dimensionality is reduced and the interpretability is preserved. Finally, we provide numerical validations of the proposed algorithm both on synthetic datasets to confirm the theoretical results and on real datasets to show some promising applications.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"86 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data","authors":"Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland","doi":"10.1007/s10618-024-01017-y","DOIUrl":"https://doi.org/10.1007/s10618-024-01017-y","url":null,"abstract":"<p>We introduce MCCE: <span>({{{underline{varvec{M}}}}})</span>onte <span>({{{underline{varvec{C}}}}})</span>arlo sampling of valid and realistic <span>({{{underline{varvec{C}}}}})</span>ounterfactual <span>({{{underline{varvec{E}}}}})</span>xplanations for tabular data, a novel counterfactual explanation method that generates on-manifold, actionable and valid counterfactuals by modeling the joint distribution of the mutable features given the immutable features and the decision. Unlike other on-manifold methods that tend to rely on variational autoencoders and have strict prediction model and data requirements, MCCE handles any type of prediction model and categorical features with more than two levels. MCCE first models the joint distribution of the features and the decision with an autoregressive generative model where the conditionals are estimated using decision trees. Then, it samples a large set of observations from this model, and finally, it removes the samples that do not obey certain criteria. We compare MCCE with a range of state-of-the-art on-manifold counterfactual methods using four well-known data sets and show that MCCE outperforms these methods on all common performance metrics and speed. In particular, including the decision in the modeling process improves the efficiency of the method substantially.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"365 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Binary quantification and dataset shift: an experimental investigation","authors":"Pablo González, Alejandro Moreo, Fabrizio Sebastiani","doi":"10.1007/s10618-024-01014-1","DOIUrl":"https://doi.org/10.1007/s10618-024-01014-1","url":null,"abstract":"<p>Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the labelled data on which the predictor has been trained and the unlabelled data are not IID, i.e., suffer from <i>dataset shift</i>. To date, quantification methods have mostly been tested only on a special case of dataset shift, i.e., <i>prior probability shift</i>; the relationship between quantification and other types of dataset shift remains, by and large, unexplored. In this work we carry out an experimental analysis of how current quantification algorithms behave under different types of dataset shift, in order to identify limitations of current approaches and hopefully pave the way for the development of more broadly applicable methods. We do this by proposing a fine-grained taxonomy of types of dataset shift, by establishing protocols for the generation of datasets affected by these types of shift, and by testing existing quantification methods on the datasets thus generated. One finding that results from this investigation is that many existing quantification methods that had been found robust to prior probability shift are not necessarily robust to other types of dataset shift. A second finding is that no existing quantification method seems to be robust enough to dealing with all the types of dataset shift we simulate in our experiments. The code needed to reproduce all our experiments is publicly available at https://github.com/pglez82/quant_datasetshift.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"159 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140167619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online concept evolution detection based on active learning","authors":"Husheng Guo, Hai Li, Lu Cong, Wenjian Wang","doi":"10.1007/s10618-024-01011-4","DOIUrl":"https://doi.org/10.1007/s10618-024-01011-4","url":null,"abstract":"<p>Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient to reflect the training data distribution, it will often further restrict the detection performance. This paper proposed a concept evolution detection method based on active learning (CE_AL). Firstly, the initial classifiers are constructed by a small number of labeled samples. The sample areas are divided into the automatic labeling and the active labeling areas according to the relationship between the classifiers of different categories. Secondly, for online new coming samples, according to their different areas, two strategies based on the automatic learning-based model labeling and active learning-based expert labeling are adopted respectively, which can improve the online learning performance with only a small number of labeled samples. Besides, the strategy of “data enhance” combined with “model enhance” is adopted to accelerate the convergence of the evolution category detection model. The experimental results show that the proposed CE_AL method can enhance the detection performance of concept evolution and realize efficient learning in an unstable environment by labeling a small number of key samples.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"23 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140147920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Marginal effects for non-linear prediction functions","authors":"Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann","doi":"10.1007/s10618-023-00993-x","DOIUrl":"https://doi.org/10.1007/s10618-023-00993-x","url":null,"abstract":"<p>Beta coefficients for linear regression models represent the ideal form of an interpretable feature effect. However, for non-linear models such as generalized linear models, the estimated coefficients cannot be interpreted as a direct feature effect on the predicted outcome. Hence, marginal effects are typically used as approximations for feature effects, either as derivatives of the prediction function or forward differences in prediction due to changes in feature values. While marginal effects are commonly used in many scientific fields, they have not yet been adopted as a general model-agnostic interpretation method for machine learning models. This may stem from the ambiguity surrounding marginal effects and their inability to deal with the non-linearities found in black box models. We introduce a unified definition of forward marginal effects (FMEs) that includes univariate and multivariate, as well as continuous, categorical, and mixed-type features. To account for the non-linearity of prediction functions, we introduce a non-linearity measure for FMEs. Furthermore, we argue against summarizing feature effects of a non-linear prediction function in a single metric such as the average marginal effect. Instead, we propose to average homogeneous FMEs within population subgroups, which serve as conditional feature effect estimates.\u0000</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"476 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140010901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning a Bayesian network with multiple latent variables for implicit relation representation","authors":"Xinran Wu, Kun Yue, Liang Duan, Xiaodong Fu","doi":"10.1007/s10618-024-01012-3","DOIUrl":"https://doi.org/10.1007/s10618-024-01012-3","url":null,"abstract":"<p>Artificial intelligence applications could be more powerful and comprehensive by incorporating the ability of inference, which could be achieved by probabilistic inference over implicit relations. It is significant yet challenging to represent implicit relations among observed variables and latent ones like disease etiologies and user preferences. In this paper, we propose the BN with multiple latent variables (MLBN) as the framework for representing the dependence relations, where multiple latent variables are incorporated to describe multi-dimensional abstract concepts. However, the efficiency of MLBN learning and effectiveness of MLBN based applications are still nontrivial due to the presence of multiple latent variables. To this end, we first propose the constraint induced and Spark based algorithm for MLBN learning, as well as several optimization strategies. Moreover, we present the concept of variation degree and further design a subgraph based algorithm for incremental learning of MLBN. Experimental results suggest that our proposed MLBN model could represent the dependence relations correctly. Our proposed method outperforms some state-of-the-art competitors for personalized recommendation, and facilitates some typical approaches to achieve better performance.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"94 24 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MMA: metadata supported multi-variate attention for onset detection and prediction","authors":"Manjusha Ravindranath, K. Selçuk Candan, Maria Luisa Sapino, Brian Appavu","doi":"10.1007/s10618-024-01008-z","DOIUrl":"https://doi.org/10.1007/s10618-024-01008-z","url":null,"abstract":"<p>Deep learning has been applied successfully in sequence understanding and translation problems, especially in univariate, unimodal contexts, where large number of supervision data are available. The effectiveness of deep learning in more complex (multi-modal, multi-variate) contexts, where supervision data is rare, however, is generally not satisfactory. In this paper, we focus on improving detection and prediction accuracy in precisely such contexts – in particular, we focus on the problem of predicting seizure onsets relying on multi-modal (EEG, ICP, ECG, and ABP) sensory data streams, some of which (such as EEG) are inherently multi-variate due to the placement of multiple sensors to capture spatial distribution of the relevant signals. In particular, we note that multi-variate time series often carry robust, spatio-temporally localized features that could help predict onset events. We further argue that such features can be used to support implementation of metadata supported multivariate attention (or MMA) mechanisms that help significantly improve the effectiveness of neural networks architectures. In this paper, we use the proposed MMA approach to develop a multi-modal LSTM-based neural network architecture to tackle seizure onset detection and prediction tasks relying on EEG, ICP, ECG, and ABP data streams. We experimentally evaluate the proposed architecture under different scenarios – the results illustrate the effectiveness of the proposed attention mechanism, especially compared against other metadata driven competitors.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"7 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139909369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural learning of simple staged trees","authors":"","doi":"10.1007/s10618-024-01007-0","DOIUrl":"https://doi.org/10.1007/s10618-024-01007-0","url":null,"abstract":"<h3>Abstract</h3> <p>Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditional independences via vertex coloring. However, since they are based on a tree representation of the sample space, the underlying graph becomes cluttered and difficult to visualize as the number of variables increases. Here, we introduce the first structural learning algorithms for the class of simple staged trees, entertaining a compact coalescence of the underlying tree from which non-symmetric independences can be easily read. We show that data-learned simple staged trees often outperform Bayesian networks in model fit and illustrate how the coalesced graph is used to identify non-symmetric conditional independences.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"256 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139751818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning","authors":"Nazanin Moradinasab, Suchetha Sharma, Ronen Bar-Yoseph, Shlomit Radom-Aizik, Kenneth C. Bilchick, Dan M. Cooper, Arthur Weltman, Donald E. Brown","doi":"10.1007/s10618-024-01006-1","DOIUrl":"https://doi.org/10.1007/s10618-024-01006-1","url":null,"abstract":"<p>The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods for MTSC tasks. The success of these approaches relies on access to the massive amount of labeled data (i.e., annotating or assigning tags to each sample that shows its corresponding category). However, obtaining a massive amount of labeled data is usually very time-consuming and expensive in many real-world applications such as medicine, because it requires domain experts’ knowledge to annotate data. Insufficient labeled data prevents these models from learning discriminative features, resulting in poor margins that reduce generalization performance. To address this challenge, we propose a novel approach: supervised contrastive learning for time series classification (SupCon-TSC). This approach improves the classification performance by learning the discriminative low-dimensional representations of multivariate time series, and its end-to-end structure allows for interpretable outcomes. It is based on supervised contrastive (SupCon) loss to learn the inherent structure of multivariate time series. First, two separate augmentation families, including strong and weak augmentation methods, are utilized to generate augmented data for the source and target networks, respectively. Second, we propose the instance-level, and cluster-level SupCon learning approaches to capture contextual information to learn the discriminative and universal representation for multivariate time series datasets. In the instance-level SupCon learning approach, for each given anchor instance that comes from the source network, the low-variance output encodings from the target network are sampled as positive and negative instances based on their labels. However, the cluster-level approach is performed between each instance and cluster centers among batches, as opposed to the instance-level approach. The cluster-level SupCon loss attempts to maximize the similarities between each instance and cluster centers among batches. We tested this novel approach on two small cardiopulmonary exercise testing (CPET) datasets and the real-world UEA Multivariate time series archive. The results of the SupCon-TSC model on CPET datasets indicate its capability to learn more discriminative features than existing approaches in situations where the size of the dataset is small. Moreover, the results on the UEA archive show that training a classifier on top of the universal representation features learned by our proposed method outperforms the state-of-the-art approaches.</p>","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"85 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139751808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}