Usage of Simplified Fuzzy ARTMAP for improvement of classification performances

Emina Alickovic, Abdulhamit Subasi


This study presents a simplified fuzzy ARTMAP (SFAM) for different classification applications. The proposed SFAM model is synergy of fuzzy logic and adaptive resonance theory (ART) neural networks. SFAM is supervised network consisting of two layers (Fuzzy ART and Inter ART) that build constant classification groups in answer to series of input patterns. Fuzzy ART layer takes a series of input patterns and relate them to output classes. Inter ART layer functions in such a way that it raises the vigilance parameter of fuzzy ART layer. By combining this two layers, SFAM is capable to perform classification very efficiently and giving very high performances. Lastly, the SFAM model is applied to different simulations. The simulation results obtained for the three different datasets: Iris, Wisconsin breast cancer and wine dataset prove that SFAM model has better performance results than other models for these classification applications.

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Copyright (c) 2015 SouthEast Europe Journal of Soft Computing

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

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This work is licensed under a Creative Commons Attribution 4.0 International License