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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2017

22.01.2016 | Theoretical Advances

Decision tree induction based on minority entropy for the class imbalance problem

verfasst von: Kesinee Boonchuay, Krung Sinapiromsaran, Chidchanok Lursinsap

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2017

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Abstract

Most well-known classifiers can predict a balanced data set efficiently, but they misclassify an imbalanced data set. To overcome this problem, this research proposes a new impurity measure called minority entropy, which uses information from the minority class. It applies a local range of minority class instances on a selected numeric attribute with Shannon’s entropy. This range defines a subset of instances concentrating on the minority class to be constructed by decision tree induction. A decision tree algorithm using minority entropy shows improvement compared with the geometric mean and F-measure over C4.5, the distinct class-based splitting measure, asymmetric entropy, a top–down decision tree and Hellinger distance decision tree on 24 imbalanced data sets from the UCI repository.
Vorheriger Artikel KPCA method based on within-class auxiliary training samples and its application to pattern classification
Nächster Artikel Approximation of fruit skin patterns using spherical Voronoi diagrams

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Metadaten
Titel
Decision tree induction based on minority entropy for the class imbalance problem
verfasst von
Kesinee Boonchuay
Krung Sinapiromsaran
Chidchanok Lursinsap
Publikationsdatum
22.01.2016
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2017
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-016-0533-3

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