NDLI: Automatic feature selection for adaptive resolution classifiers

Content Provider	IEEE Xplore Digital Library
Author	Rizzi, A. Panella, M. Mascioli, F.M.F. Martinelli, G.
Copyright Year	2002
Description	Author affiliation: INFOCOM Dept., Rome Univ., Italy (Rizzi, A.; Panella, M.; Mascioli, F.M.F.; Martinelli, G.)
Abstract	Classification can be considered as a basic data driven modeling problem, which allows us to define and design more complex modeling systems. The choice of an adequate classification system should take into account the automation degree of the learning procedure, especially if it must be employed as a core inference engine. Fuzzy min-max neural networks are very effective and flexible classification models, since they easily allow the design of constructive learning techniques, such as the ARC/PARC one. In this paper we propose a classification system able to generate automatically a fuzzy min-max classifier. It holds the capability to optimize both the number of neurons in the hidden layer and the set of features used to classify a pattern, without any knowledge about the test set. Its performances are evaluated through a toy problem and two real data benchmarks.
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	384
Ending Page	389
File Size	484120
Page Count	6
File Format	PDF
ISBN	0780372808
DOI	10.1109/FUZZ.2002.1005021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Electronic mail Design automation Engines Fuzzy neural networks Neural networks Fuzzy systems Neurons Benchmark testing Genetic algorithms
Content Type	Text
Resource Type	Article

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