CART data analysis to attain interpretability in a Fuzzy Logic Classifier

Guido Vagliasindi, Paolo Arena, Andrea Murari

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

A data driven methodology to automatically derive a Fuzzy Logic Classifier (FLC) only on the basis of the raw signals available, is proposed. The first step is a feature selection performed with the approach of Classification and Regression Trees (CART), to extract the variables in the database which are the most critical for the problem under study. Then a CART is produced using only the previously selected features and is provided to a fully automated algorithm which determines the membership functions and the most appropriate rules to reproduce the classification tree obtained with CART. The resulting FLC attains good performance in terms of generalization and classification, still providing a set of rules which can be easily interpreted in order to achieve a first, intuitive understanding of the phenomenon involved. To assess the potentiality of the approach, the method has been applied to a synthetic database provided for the NIPS 2003 feature selection competition and to a real classification problem. © 2009 IEEE.
Original languageEnglish
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - , United States
Duration: 1 Jan 2009 → …

Conference

Conference2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
Period1/1/09 → …

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Vagliasindi, G., Arena, P., & Murari, A. (2009). CART data analysis to attain interpretability in a Fuzzy Logic Classifier. Paper presented at 2009 International Joint Conference on Neural Networks, IJCNN 2009, United States. https://doi.org/10.1109/IJCNN.2009.5178855