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

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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.

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Acknowledgments

We thank Strategic scholarships Fellowships Frontier Research Networks (specific for Southern region) for the Ph.D. Program Thai doctoral degree from the Commission on Higher Education, Thailand for its financial support.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand

    Kesinee Boonchuay, Krung Sinapiromsaran & Chidchanok Lursinsap

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  1. Kesinee Boonchuay
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  2. Krung Sinapiromsaran
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  3. Chidchanok Lursinsap
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Correspondence to Krung Sinapiromsaran.

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Boonchuay, K., Sinapiromsaran, K. & Lursinsap, C. Decision tree induction based on minority entropy for the class imbalance problem. Pattern Anal Applic 20, 769–782 (2017). https://doi.org/10.1007/s10044-016-0533-3

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