Fuzzy ARTMAP rule extraction in computational chemistry

Authors:
Razvan Andonie;Levente Fabry-Asztalos;Bogdan Crivat;Sarah Abdul-Wahid;Badi' Abdul-Wahid
Affiliations:
Computer Science Department, Central Washington University, Ellensburg and Electronics and Computers Department, Transylvania University of Brasov, Romania;Department of Chemistry, Central Washington University, Ellensburg;Microsoft Corporation, Redmond, WA;Computer Science Department, Central Washington University, Ellensburg;Computer Science Department and Department of Chemistry, Central Washington University, Ellensburg
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

We focus on extracting rules from a trained FAMR model. The FAMR is a Fuzzy ARTMAP (FAM) incremental learning system used for classification, probability estimation, and function approximation. The set of rules generated is post-processed in order to improve its generalization capability. Our method is suitable for small training sets. We compare our method with another neuro-fuzzy algorithm, and two standard decision tree algorithms: CART trees and Microsoft Decision Trees. Our goal is to improve efficiency of drug discovery, by providing medicinal chemists with a predictive tool for bioactivity of HIV-1 protease inhibitors.