Monotone Classification with Decision Trees

DOI

10.2991/eusflat.2013.120How to use a DOI?

Keywords

Fuzzy decision tree induction. Monotone classification. Measures of discrimination.

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Marsala Christophe
AU  - Davide Petturiti
PY  - 2013/08
DA  - 2013/08
TI  - Monotone Classification with Decision Trees
BT  - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SP  - 850
EP  - 857
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.120
DO  - 10.2991/eusflat.2013.120
ID  - Christophe2013/08
ER  -