Title
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.
Recommended Citation
Andonie, R., Fabry-Asztalos, L., Crivat, B., Abdul-Wahid, S., & Abdul-Wahid, B. ’. (2009). Fuzzy ARTMAP rule extraction in computational chemistry. 2009 International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.2009.5179007
Journal
2009 International Joint Conference on Neural Networks
Rights
Copyright © 2009, IEEE
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.