Fuzzy ARTMAP Rule Extraction in Computational Chemistry

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



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.


This proceeding was originally published in 2009 International Joint Conference on Neural Networks. The article from the publisher can be found here.

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2009 International Joint Conference on Neural Networks


Copyright © 2009, IEEE