Fuzzy ARTMAP Rule Extraction in Computational Chemistry

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date

6-9-2009

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.

Comments

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

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Journal

2009 International Joint Conference on Neural Networks

Rights

Copyright © 2009, IEEE

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