Interpretability improvement of fuzzy rule-based classifiers via rule compression

DOI

10.2991/ifsa-eusflat-15.2015.26How to use a DOI?

Keywords

Rule-based classification, feature selection, interpretability

Abstract

Rule-level feature selection, also termed as rule compression, is an important technique for improving interpretability of fuzzy rule-based classifiers. In this paper we present three different rule compression algorithms and analyze their performance and characteristics on the classifiers identified from wellknown classification benchmarks, namely the Iris, Wine and two versions of Wisconsin Breast Cancer data sets. Our study shows that the classifiers, in which the overlap between either the rules representing different classes or all rules is eliminated, can be usually compressed at a higher rate and that the interpretation of such classifiers is more insightful.

Copyright

© 2015, 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  - Andri Riid
AU  - Jürgo-Sören Preden
PY  - 2015/06
DA  - 2015/06
TI  - Interpretability improvement of fuzzy rule-based classifiers via rule compression
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 162
EP  - 169
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.26
DO  - 10.2991/ifsa-eusflat-15.2015.26
ID  - Riid2015/06
ER  -