NDLI: A bootstrap-based aggregate classifier for model-based clustering

Content Provider	Springer Nature Link
Author	Dias, José G. Vermunt, Jeroen K.
Copyright Year	2008
Abstract	In model-based clustering, a situation in which true class labels are unknown and that is therefore also referred to as unsupervised learning, observations are typically classified by the Bayes modal rule. In this study, we assess whether alternative classifiers from the classification or supervised-learning literature—developed for situations in which class labels are known—can improve the Bayes rule. More specifically, we investigate the performance of bootstrap-based aggregate (bagging) rules after adapting these to the model-based clustering context. It is argued that specific issues, such as the label-switching problem, have to be carefully addressed when using bootstrap methods in model-based clustering. Our two Monte Carlo studies show that classification based on the Bayes rule is rather stable and difficult to improve by bootstrap-based aggregate rules, even for sparse data. An empirical example illustrates the various approaches described in this paper.
Starting Page	643
Ending Page	659
Page Count	17
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	23
Issue Number	4
e-ISSN	16139658
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-02-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification rules Classifiers Bootstrap estimation Model-based clustering Latent class model Economic Theory Probability Theory and Stochastic Processes Probability and Statistics in Computer Science Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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