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Computing Finite Mixture Estimators in the Tails 计算尾部的有限混合估计量
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2023-04-13 DOI: 10.1007/s00357-023-09433-3
Marilena Furno
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引用次数: 0
Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models. 广义线性模型混合的局部和总体偏差R平方测度。
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2023-04-04 DOI: 10.1007/s00357-023-09432-4
Roberto Di Mari, Salvatore Ingrassia, Antonio Punzo
{"title":"Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models.","authors":"Roberto Di Mari,&nbsp;Salvatore Ingrassia,&nbsp;Antonio Punzo","doi":"10.1007/s00357-023-09432-4","DOIUrl":"10.1007/s00357-023-09432-4","url":null,"abstract":"<p><p>In generalized linear models (GLMs), measures of lack of fit are typically defined as the deviance between two nested models, and a deviance-based <i>R</i><sup>2</sup> is commonly used to evaluate the fit. In this paper, we extend deviance measures to mixtures of GLMs, whose parameters are estimated by maximum likelihood (ML) via the EM algorithm. Such measures are defined both locally, i.e., at cluster-level, and globally, i.e., with reference to the whole sample. At the cluster-level, we propose a normalized two-term decomposition of the local deviance into explained, and unexplained local deviances. At the sample-level, we introduce an additive normalized decomposition of the total deviance into three terms, where each evaluates a different aspect of the fitted model: (1) the cluster separation on the dependent variable, (2) the proportion of the total deviance explained by the fitted model, and (3) the proportion of the total deviance which remains unexplained. We use both local and global decompositions to define, respectively, local and overall deviance <i>R</i><sup>2</sup> measures for mixtures of GLMs, which we illustrate-for Gaussian, Poisson and binomial responses-by means of a simulation study. The proposed fit measures are then used to assess, and interpret clusters of COVID-19 spread in Italy in two time points.</p>","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":" ","pages":"1-34"},"PeriodicalIF":2.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9768843","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
Characteristics of Distance Matrices Based on Euclidean, Manhattan and Hausdorff Coefficients 基于欧几里得、曼哈顿和豪斯多夫系数的距离矩阵特征
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2023-04-03 DOI: 10.1007/s00357-023-09435-1
J. T. Temple, R. Bateman
{"title":"Characteristics of Distance Matrices Based on Euclidean, Manhattan and Hausdorff Coefficients","authors":"J. T. Temple, R. Bateman","doi":"10.1007/s00357-023-09435-1","DOIUrl":"https://doi.org/10.1007/s00357-023-09435-1","url":null,"abstract":"","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"214 - 232"},"PeriodicalIF":2.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46807267","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: 2
Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance 找到谚语的针：在极端阶级失衡下提高少数民族的阶级认同
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2023-02-23 DOI: 10.1007/s00357-023-09431-5
Trent Geisler, Herman Ray, Ying Xie
{"title":"Finding the Proverbial Needle: Improving Minority Class Identification Under Extreme Class Imbalance","authors":"Trent Geisler, Herman Ray, Ying Xie","doi":"10.1007/s00357-023-09431-5","DOIUrl":"https://doi.org/10.1007/s00357-023-09431-5","url":null,"abstract":"","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"192-212"},"PeriodicalIF":2.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46841940","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
Classification Trees with Mismeasured Responses 具有误判响应的分类树
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2023-02-16 DOI: 10.1007/s00357-023-09430-6
L. Diao, Grace Y. Yi
{"title":"Classification Trees with Mismeasured Responses","authors":"L. Diao, Grace Y. Yi","doi":"10.1007/s00357-023-09430-6","DOIUrl":"https://doi.org/10.1007/s00357-023-09430-6","url":null,"abstract":"","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"168-191"},"PeriodicalIF":2.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44301135","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
Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data 有序评级数据二项回归树的不确定性诊断
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2023-01-21 DOI: 10.1007/s00357-022-09429-5
R. Simone
{"title":"Uncertainty Diagnostics of Binomial Regression Trees for Ordered Rating Data","authors":"R. Simone","doi":"10.1007/s00357-022-09429-5","DOIUrl":"https://doi.org/10.1007/s00357-022-09429-5","url":null,"abstract":"","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"79-105"},"PeriodicalIF":2.0,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47005228","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
DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling. DDCAL:基于迭代特征缩放的数据均匀分布到低方差聚类。
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2023-01-01 DOI: 10.1007/s00357-022-09428-6
Marian Lux, Stefanie Rinderle-Ma
{"title":"DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling.","authors":"Marian Lux,&nbsp;Stefanie Rinderle-Ma","doi":"10.1007/s00357-022-09428-6","DOIUrl":"https://doi.org/10.1007/s00357-022-09428-6","url":null,"abstract":"<p><p>This work studies the problem of clustering one-dimensional data points such that they are evenly distributed over a given number of low variance clusters. One application is the visualization of data on choropleth maps or on business process models, but without over-emphasizing outliers. This enables the detection and differentiation of smaller clusters. The problem is tackled based on a heuristic algorithm called DDCAL (1d distribution cluster algorithm) that is based on iterative feature scaling which generates stable results of clusters. The effectiveness of the DDCAL algorithm is shown based on 5 artificial data sets with different distributions and 4 real-world data sets reflecting different use cases. Moreover, the results from DDCAL, by using these data sets, are compared to 11 existing clustering algorithms. The application of the DDCAL algorithm is illustrated through the visualization of pandemic and population data on choropleth maps as well as process mining results on process models.</p>","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"106-144"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9476660","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: 1
A Semi-parametric Density Estimation with Application in Clustering 半参数密度估计及其在聚类中的应用
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2022-12-14 DOI: 10.1007/s00357-022-09425-9
M. Salehi, A. Bekker, M. Arashi
{"title":"A Semi-parametric Density Estimation with Application in Clustering","authors":"M. Salehi, A. Bekker, M. Arashi","doi":"10.1007/s00357-022-09425-9","DOIUrl":"https://doi.org/10.1007/s00357-022-09425-9","url":null,"abstract":"","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"52-78"},"PeriodicalIF":2.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48188739","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
Merging Components in Linear Gaussian Cluster-Weighted Models 线性高斯聚类加权模型中的分量合并
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2022-12-07 DOI: 10.1007/s00357-022-09424-w
Sangkon Oh, Byungtae Seo
{"title":"Merging Components in Linear Gaussian Cluster-Weighted Models","authors":"Sangkon Oh, Byungtae Seo","doi":"10.1007/s00357-022-09424-w","DOIUrl":"https://doi.org/10.1007/s00357-022-09424-w","url":null,"abstract":"","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"25-51"},"PeriodicalIF":2.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49126059","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: 1
Imputation Strategies for Clustering Mixed-Type Data with Missing Values 缺失值混合数据聚类的插值策略
IF 2 4区 计算机科学
Journal of Classification Pub Date : 2022-11-26 DOI: 10.1007/s00357-022-09422-y
Rabea Aschenbruck, G. Szepannek, A. Wilhelm
{"title":"Imputation Strategies for Clustering Mixed-Type Data with Missing Values","authors":"Rabea Aschenbruck, G. Szepannek, A. Wilhelm","doi":"10.1007/s00357-022-09422-y","DOIUrl":"https://doi.org/10.1007/s00357-022-09422-y","url":null,"abstract":"","PeriodicalId":50241,"journal":{"name":"Journal of Classification","volume":"40 1","pages":"2-24"},"PeriodicalIF":2.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46679720","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: 2
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