Monotone Classification with Decision Trees

Christophe Marsala 1 Davide Petturiti
1 LFI - Learning, Fuzzy and Intelligent systems
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In machine learning, monotone classification is concerned with a classification function to learn in order to guarantee a kind of monotonicity of the class with respect to attribute values. In this paper, we focus on rank discrimination measures to be used in decision tree induction, i.e., functions able to measure the discrimination power of an attribute with respect to the class taking into account the monotonicity of the class with respect to the attribute. Three new measures are studied in detail and an experimental analysis is also provided, comparing the proposed approach with other well-known monotone and non-monotone classifiers in terms of classification accuracy.
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Submitted on : Friday, October 16, 2015 - 1:47:28 PM
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Christophe Marsala, Davide Petturiti. Monotone Classification with Decision Trees. The 8th conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2013, Sep 2013, Milan, Italy. pp.810-817, ⟨10.2991/eusflat.2013.120⟩. ⟨hal-01216515⟩



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