NDLI: Feature Selection Using a Hybrid Associative Classifier with Masking Techniques

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Feature Selection Using a Hybrid Associative Classifier with Masking Techniques

Content Provider	IEEE Xplore Digital Library
Author	Aldape-Perez, M. Yanez-Marquez, C. Lopez Leyva, L.O.
Copyright Year	2006
Description	Author affiliation: CIC-IPN, Mexico (Aldape-Perez, M.; Yanez-Marquez, C.; Lopez Leyva, L.O.)
Abstract	Performance in most pattern classifiers is improved when redundant or irrelevant features are removed, however, this is mainly achieved by successive classifiers construction. In this paper hybrid classification and masking techniques are presented as a new feature selection approach. The algorithm uses a hybrid classifier to provide a mask that identifies the optimal subset of features without having to compute a new classifier at each step. This method allows us to identify irrelevant or redundant features for classification purposes. Our results suggest that this method is shown to be a feasible way to identify optimal subset of features.
Starting Page	151
Ending Page	160
File Size	238476
Page Count	10
File Format	PDF
ISBN	0769527221
DOI	10.1109/MICAI.2006.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-13
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Associative memory Costs Error analysis Neural networks Humans Multilayer perceptrons Feature extraction Multi-layer neural network Principal component analysis
Content Type	Text
Resource Type	Article

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