Unsupervised feature selection for interpretable classification in behavioral assessment of children

In this paper, we consider a data set taken from the administration of the Behavior Assessment System for Children test to 157 subjects, and we approach the problem of clustering and classify the subjects in an interpretable fashion. Because the Behavior Assessment System for Children test is origin...

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Veröffentlicht in:	Expert systems Jg. 34; H. 4
Hauptverfasser:	Jiménez, Fernando, Jódar, Rosalia, Martín, Maria del Pilar, Sánchez, Gracia, Sciavicco, Guido
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Blackwell Publishing Ltd 01.08.2017
Schlagworte:	Accuracy Algorithms Behavior Assessment System for Children Children Classification Classifiers Clustering Decision making Evolutionary algorithms Feature selection fuzzy classification Genetic algorithms Indexing Iterative methods Maximization Multiple objective analysis multi‐objective evolutionary computation unsupervised feature selection for classification
ISSN:	0266-4720, 1468-0394
Online-Zugang:	Volltext
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