Advances in Data Analysis and Classification最新文献_第8页

Multidimensional scaling for big data 大数据的多维扩展

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-04-13 DOI: 10.1007/s11634-024-00591-9

Pedro Delicado, Cristian Pachón-García

{"title":"Multidimensional scaling for big data","authors":"Pedro Delicado, Cristian Pachón-García","doi":"10.1007/s11634-024-00591-9","DOIUrl":"10.1007/s11634-024-00591-9","url":null,"abstract":"<div>We present a set of algorithms implementing multidimensional scaling (MDS) for large data sets. MDS is a family of dimensionality reduction techniques using a (n times n) distance matrix as input, where n is the number of individuals, and producing a low dimensional configuration: a (ntimes r) matrix with (r<<n). When n is large, MDS is unaffordable with classical MDS algorithms because their extremely large memory and time requirements. We compare six non-standard algorithms intended to overcome these difficulties. They are based on the central idea of partitioning the data set into small pieces, where classical MDS methods can work. Two of these algorithms are original proposals. In order to check the performance of the algorithms as well as to compare them, we have done a simulation study. Additionally, we have used the algorithms to obtain an MDS configuration for EMNIST: a real large data set with more than 800000 points. We conclude that all the algorithms are appropriate to use for obtaining an MDS configuration, but we recommend to use one of our proposals, since it is a fast algorithm with satisfactory statistical properties when working with big data. An R package implementing the algorithms has been created.</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"649 - 670"},"PeriodicalIF":1.3,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00591-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140588671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

View selection in multi-view stacking: choosing the meta-learner 多视图叠加中的视图选择：选择元学习器

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-04-12 DOI: 10.1007/s11634-024-00587-5

Wouter van Loon, Marjolein Fokkema, Botond Szabo, Mark de Rooij

{"title":"View selection in multi-view stacking: choosing the meta-learner","authors":"Wouter van Loon, Marjolein Fokkema, Botond Szabo, Mark de Rooij","doi":"10.1007/s11634-024-00587-5","DOIUrl":"10.1007/s11634-024-00587-5","url":null,"abstract":"<div>Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learner algorithm is trained on each view separately, and their predictions are then combined by a meta-learner algorithm. In a previous study, stacked penalized logistic regression, a special case of multi-view stacking, has been shown to be useful in identifying which views are most important for prediction. In this article we expand this research by considering seven different algorithms to use as the meta-learner, and evaluating their view selection and classification performance in simulations and two applications on real gene-expression data sets. Our results suggest that if both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners. Exactly which among these three is to be preferred depends on the research context. The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.\u0000</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"579 - 617"},"PeriodicalIF":1.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00587-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural-neighborhood based, label-specific undersampling for imbalanced, multi-label data 基于自然邻域的特定标签欠采样，用于不平衡多标签数据

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-03-30 DOI: 10.1007/s11634-024-00589-3

Payel Sadhukhan, Sarbani Palit

{"title":"Natural-neighborhood based, label-specific undersampling for imbalanced, multi-label data","authors":"Payel Sadhukhan, Sarbani Palit","doi":"10.1007/s11634-024-00589-3","DOIUrl":"10.1007/s11634-024-00589-3","url":null,"abstract":"<div>This work presents a novel undersampling scheme to tackle the imbalance problem in multi-label datasets. We use the principles of the natural nearest neighborhood and follow a paradigm of label-specific undersampling. Natural-nearest neighborhood is a parameter-free principle. Our scheme’s novelty lies in exploring the parameter-optimization-free natural nearest neighborhood principles. The class imbalance problem is particularly challenging in a multi-label context, as the imbalance ratio and the majority–minority distributions vary from label to label. Consequently, the majority–minority class overlaps also vary across the labels. Working on this aspect, we propose a framework where a single natural neighbor search is sufficient to identify all the label-specific overlaps. Natural neighbor information is also used to find the key lattices of the majority class (which we do not undersample). The performance of the proposed method, NaNUML, indicates its ability to mitigate the class-imbalance issue in multi-label datasets to a considerable extent. We could also establish a statistically superior performance over other competing methods several times. An empirical study involving twelve real-world multi-label datasets, seven competing methods, and four evaluating metrics—shows that the proposed method effectively handles the class-imbalance issue in multi-label datasets. In this work, we have presented a novel label-specific undersampling scheme, NaNUML, for multi-label datasets. NaNUML is based on the parameter-free natural neighbor search and the key factor, neighborhood size ‘k’ is determined without invoking any parameter optimization.</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"18 3","pages":"723 - 744"},"PeriodicalIF":1.4,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140363801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy-based fuzzy clustering of interval-valued time series 基于熵的区间值时间序列模糊聚类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-03-29 DOI: 10.1007/s11634-024-00586-6

Vincenzina Vitale, Pierpaolo D’Urso, Livia De Giovanni, Raffaele Mattera

引用次数: 0

Clustering ensemble extraction: a knowledge reuse framework 聚类组合提取：知识再利用框架

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-03-27 DOI: 10.1007/s11634-024-00588-4

Mohaddeseh Sedghi, Ebrahim Akbari, Homayun Motameni, Touraj Banirostam

{"title":"Clustering ensemble extraction: a knowledge reuse framework","authors":"Mohaddeseh Sedghi, Ebrahim Akbari, Homayun Motameni, Touraj Banirostam","doi":"10.1007/s11634-024-00588-4","DOIUrl":"10.1007/s11634-024-00588-4","url":null,"abstract":"<div>Clustering ensemble combines several fundamental clusterings with a consensus function to produce the final clustering without gaining access to data features. The quality and diversity of a vast library of base clusterings influence the performance of the consensus function. When a huge library of various clusterings is not available, this function produces results of lower quality than those of the basic clustering. The expansion of diverse clusters in the collection to increase the performance of consensus, especially in cases where there is no access to specific data features or assumptions in the data distribution, has still remained an open problem. The approach proposed in this paper, Clustering Ensemble Extraction, considers the similarity criterion at the cluster level and places the most similar clusters in the same group. Then, it extracts new clusters with the help of the Extracting Clusters Algorithm. Finally, two new consensus functions, namely Cluster-based extracted partitioning algorithm and Meta-cluster extracted algorithm, are defined and then applied to new clusters in order to create a high-quality clustering. The results of the empirical experiments conducted in this study showed that the new consensus function obtained by our proposed method outperformed the methods previously proposed in the literature regarding the clustering quality and efficiency.</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 3","pages":"551 - 578"},"PeriodicalIF":1.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixtures of regressions using matrix-variate heavy-tailed distributions 使用矩阵变量重尾分布的回归混合物

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-03-16 DOI: 10.1007/s11634-024-00585-7

Salvatore D. Tomarchio, Michael P. B. Gallaugher

{"title":"Mixtures of regressions using matrix-variate heavy-tailed distributions","authors":"Salvatore D. Tomarchio, Michael P. B. Gallaugher","doi":"10.1007/s11634-024-00585-7","DOIUrl":"10.1007/s11634-024-00585-7","url":null,"abstract":"<div>Finite mixtures of regressions (FMRs) are powerful clustering devices used in many regression-type analyses. Unfortunately, real data often present atypical observations that make the commonly adopted normality assumption of the mixture components inadequate. Thus, to robustify the FMR approach in a matrix-variate framework, we introduce ten FMRs based on the matrix-variate t and contaminated normal distributions. Furthermore, once one of our models is estimated and the observations are assigned to the groups, different procedures can be used for the detection of the atypical points in the data. An ECM algorithm is outlined for maximum likelihood parameter estimation. By using simulated data, we show the negative consequences (in terms of parameter estimates and inferred classification) of the wrong normality assumption in the presence of heavy-tailed clusters or noisy matrices. Such issues are properly addressed by our models instead. Additionally, over the same data, the atypical points detection procedures are also investigated. A real-data analysis concerning the relationship between greenhouse gas emissions and their determinants is conducted, and the behavior of our models in the presence of heterogeneity and atypical observations is discussed.</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 :","pages":"11 - 38"},"PeriodicalIF":1.3,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00585-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140147449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clustering by deep latent position model with graph convolutional network 利用图卷积网络的深度潜位置模型进行聚类

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-03-12 DOI: 10.1007/s11634-024-00583-9

Dingge Liang, Marco Corneli, Charles Bouveyron, Pierre Latouche

{"title":"Clustering by deep latent position model with graph convolutional network","authors":"Dingge Liang, Marco Corneli, Charles Bouveyron, Pierre Latouche","doi":"10.1007/s11634-024-00583-9","DOIUrl":"10.1007/s11634-024-00583-9","url":null,"abstract":"<div>With the significant increase of interactions between individuals through numeric means, clustering of nodes in graphs has become a fundamental approach for analyzing large and complex networks. In this work, we propose the deep latent position model (DeepLPM), an end-to-end generative clustering approach which combines the widely used latent position model (LPM) for network analysis with a graph convolutional network encoding strategy. Moreover, an original estimation algorithm is introduced to integrate the explicit optimization of the posterior clustering probabilities via variational inference and the implicit optimization using stochastic gradient descent for graph reconstruction. Numerical experiments on simulated scenarios highlight the ability of DeepLPM to self-penalize the evidence lower bound for selecting the number of clusters, demonstrating its clustering capabilities compared to state-of-the-art methods. Finally, DeepLPM is further applied to an ecclesiastical network in Merovingian Gaul and to a citation network Cora to illustrate the practical interest in exploring large and complex real-world networks.</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 1","pages":"237 - 270"},"PeriodicalIF":1.4,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140126978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion 通过新型分层贝叶斯信息准则选择不完整数据因子分析中的因子数量

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-03-07 DOI: 10.1007/s11634-024-00582-w

Jianhua Zhao, Changchun Shang, Shulan Li, Ling Xin, Philip L. H. Yu

{"title":"Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion","authors":"Jianhua Zhao, Changchun Shang, Shulan Li, Ling Xin, Philip L. H. Yu","doi":"10.1007/s11634-024-00582-w","DOIUrl":"10.1007/s11634-024-00582-w","url":null,"abstract":"<div>The Bayesian information criterion (BIC), defined as the observed data log likelihood minus a penalty term based on the sample size N, is a popular model selection criterion for factor analysis with complete data. This definition has also been suggested for incomplete data. However, the penalty term based on the ‘complete’ sample size N is the same no matter whether in a complete or incomplete data case. For incomplete data, there are often only (N_i<N) observations for variable i, which means that using the ‘complete’ sample size N implausibly ignores the amounts of missing information inherent in incomplete data. Given this observation, a novel hierarchical BIC (HBIC) criterion is proposed for factor analysis with incomplete data, which is denoted by HBICinc. The novelty is that HBICinc only uses the actual amounts of observed information, namely (N_i)’s, in the penalty term. Theoretically, it is shown that HBICinc is a large sample approximation of variational Bayesian (VB) lower bound, and BIC is a further approximation of HBICinc, which means that HBICinc shares the theoretical consistency of BIC. Experiments on synthetic and real data sets are conducted to access the finite sample performance of HBICinc, BIC, and related criteria with various missing rates. The results show that HBICinc and BIC perform similarly when the missing rate is small, but HBICinc is more accurate when the missing rate is not small.\u0000</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 1","pages":"209 - 235"},"PeriodicalIF":1.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00582-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140057185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements 基于预期一致指数无偏估计器的各种卡帕系数估计器

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-03-06 DOI: 10.1007/s11634-024-00581-x

A. Martín Andrés, M. Álvarez Hernández

{"title":"Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements","authors":"A. Martín Andrés, M. Álvarez Hernández","doi":"10.1007/s11634-024-00581-x","DOIUrl":"10.1007/s11634-024-00581-x","url":null,"abstract":"<div>To measure the degree of agreement between R observers who independently classify n subjects within K categories, various kappa-type coefficients are often used. When R = 2, it is common to use the Cohen' kappa, Scott's pi, Gwet’s AC1/2, and Krippendorf's alpha coefficients (weighted or not). When R > 2, some pairwise version based on the aforementioned coefficients is normally used; with the same order as above: Hubert's kappa, Fleiss's kappa, Gwet's AC1/2, and Krippendorf's alpha. However, all these statistics are based on biased estimators of the expected index of agreements, since they estimate the product of two population proportions through the product of their sample estimators. The aims of this article are three. First, to provide statistics based on unbiased estimators of the expected index of agreements and determine their variance based on the variance of the original statistic. Second, to make pairwise extensions of some measures. And third, to show that the old and new estimators of the Cohen’s kappa and Hubert’s kappa coefficients match the well-known estimators of concordance and intraclass correlation coefficients, if the former are defined by assuming quadratic weights. The article shows that the new estimators are always greater than or equal the classic ones, except for the case of Gwet where it is the other way around, although these differences are only relevant with small sample sizes (e.g. n ≤ 30).</div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 1","pages":"177 - 207"},"PeriodicalIF":1.4,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-024-00581-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140044390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Special issue on “advances in models and learning for clustering and classification” "聚类和分类模型与学习的进展 "特刊

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-02-27 DOI: 10.1007/s11634-024-00584-8

Luis-Angel García-Escudero, Salvatore Ingrassia, T. Brendan Murphy

引用次数: 0