Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Interpretability improvement of fuzzy rule-based classifiers via rule compression

Authors
Andri Riid, Jürgo-Sören Preden
Corresponding Author
Andri Riid
Available Online June 2015.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

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  - 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.26
DO  - https://doi.org/10.2991/ifsa-eusflat-15.2015.26
ID  - Riid2015/06
ER  -