Abstract
Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a novel neuro-fuzzy scheme for classification with online feature selection. It is a four-layered feed-forward network for fuzzy rule based classification. The network learns the classification rules from the training data as well selects the important features. The rules learned by the network can be easily read from the network. The system is tested on both synthetic and real data and found to perform quite well.
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© 2002 Springer-Verlag Berlin Heidelberg
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Chakraborty, D., Pal, N.R. (2002). Designing Rule-Based Classifiers with On-Line Feature Selection: A Neuro-fuzzy Approach. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_34
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DOI: https://doi.org/10.1007/3-540-45631-7_34
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