Fuzzy ARTMAP rule extraction in computational chemistry
Răzvan Andonie, Levente Fabry‐Asztalos, Bogdan Crivat, Sarah Abdul-Wahid, Badi’ Abdul-Wahid · 2009
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.