Fuzzy ARTMAP Rule Extraction in Computational Chemistry
Department or Administrative Unit
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.
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
2009 International Joint Conference on Neural Networks
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