NDLI: Automatic training of generalized min-max classifiers

Content Provider	IEEE Xplore Digital Library
Author	Rizzi, A. Panella, M. Mascioli, F.M.F. Martinelli, G.
Copyright Year	2001
Description	Author affiliation: Dept. of INFO-COM, La Sapienza Univ., Rome, Italy (Rizzi, A.)
Abstract	Among fuzzy classifiers, min-max networks have the advantage to be trained in a constructive way, by a simple learning procedure. The classification strategy of Simpson's min-max classifier (1992) consists in covering the training data with hyperboxes constrained to have their boundary surfaces parallel to the coordinate axes of the chosen reference system. In order to obtain a more accurate data coverage, it is possible to adopt a new classification model which allows to arrange the hyperboxes orientation along any direction of the data space. The training algorithm is based on the ARC/PARC technique, which already yields better performances with respect to the original Simpson's algorithm. Although the most important feature of a classifier is its generalization capability, the effectiveness of a training procedure is strictly related to its automation degree. A low automation degree can be a serious drawback for a classification system, since it can prevent an unskilled user from successfully generate an acceptable model. From this point of view, a learning procedure should not depend on any critical parameter. The automation degree of the new classification system is evaluated in the paper.
Sponsorship	Int. Fuzzy Syst. Assoc.
Starting Page	3070
Ending Page	3075
File Size	534038
Page Count	6
File Format	PDF
ISBN	0780370783
DOI	10.1109/NAFIPS.2001.943718
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Particle measurements Training data Plasma welding Testing Yield estimation Design automation Classification algorithms Power system modeling Neural networks
Content Type	Text
Resource Type	Article

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