Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009 978-1-4244-3553-1/09/$25.00 2009 IEEE
 

Summary: Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009
978-1-4244-3553-1/09/$25.00 ©2009 IEEE
Fuzzy ARTMAP Rule Extraction in Computational Chemistry
Razvan Andonie, Levente Fabry-Asztalos, Bogdan Crivat¸, Sarah Abdul-Wahid, and Badi` Abdul-Wahid
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.
I. INTRODUCTION
Several neural architectures have been successful for
QSPR (Quantitative Structure-Property Relationship) and
QSAR (Quantitative Structure-Activity Relationship) tasks.
Among these are Fuzzy ARTMAP (FAM) architectures [1]­

  

Source: Andonie, Razvan - Department of Computer Science, Central Washington University

 

Collections: Computer Technologies and Information Sciences