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Author(s): Lim, C.P. | Lai, W.K. | Loy, C.C. | Tan, C.P. |
Year: 2008
Citation: International Joint Conference on Neural Networks (IJCNN) Pages: 2405-2412
Abstract: This paper presents a Fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as Modular Adaptive Resonance Theory Map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness of the proposed architecture is analyzed and compared with ARTMAP-FD network, FAM network, and One-Against-One Support Vector Machine (OAO-SVM). Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts.
Topics:
Machine Learning,
Models:
Fuzzy ARTMAP,
Modified ART,
