Paper Info
Title
Fuzzy ARTMAP rule extraction in computational chemistry
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.
Year
DOI
Venue
2009
10.1109/IJCNN.2009.5179007
IJCNN
Keywords
Field
DocType
fuzzy artmap rule extraction,incremental learning system,function approximation,fuzzy artmap,cart tree,generalization capability,drug discovery,trained famr model,medicinal chemist,microsoft decision trees,hiv-1 protease inhibitor,computational chemistry,accuracy,genetic algorithms,decision tree,classification algorithms,prediction algorithms,neural networks,computer science,decision trees,learning artificial intelligence,neuro fuzzy,data mining,chemistry,chemicals,fuzzy set theory
Decision tree,Function approximation,Computer science,Fuzzy logic,Incremental learning,Fuzzy set,Artificial intelligence,Statistical classification,Artificial neural network,Genetic algorithm,Machine learning
Conference
ISSN
ISBN
Citations 
2161-4393
978-1-4244-3553-1
1
PageRank 
References 
Authors
0.35
13
7
Name
Order
Citations
PageRank
Razvan Andonie111717.71
Levente Fabry-asztalos2203.21
Bogdan Crivat310.35
Sarah Abdul-wahid4212.56
Badi' Abdul-Wahid520.73
Fabry-Asztalos, L.610.35
Abdul-Wahid, S.710.35