Interpretability improvement of fuzzy rule-based classifiers via rule compression
Andri Riid, Jürgo-Sören Preden
Available Online June 2015.
- https://doi.org/10.2991/ifsa-eusflat-15.2015.26How to use a DOI?
- Rule-based classification, feature selection, interpretability
- 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.
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 - 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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.26 ID - Riid2015/06 ER -