NDLI: Feature Selection for Automatic Classification of Non-Gaussian Data

Content Provider	IEEE Xplore Digital Library
Author	Foroutan, Iman Sklansky, Jack
Copyright Year	1971
Description	A computer-based technique for automatic selection of features for the classification of non-Gaussian data is presented. The selection technique exploits interactive cluster finding and a modified branch and bound optimization of piecewise linear classifiers. The technique first finds an efficient set of pairs of oppositely classified clusters to represent the data. Then a zero-one implicit enumeration implements a branch and bound search for a good subset of features. A test of the feature selection technique on multidimensional synthetic and real data yielded close-to-optimum, and in many cases optimum, subsets of features. The real data consisted of a) 1284 12-dimensional feature vectors representing normal and abnormal breast tissue, extracted from X-ray mammograms, and b) 1060 30-dimensional feature vectors representing tanks and clutter in infrared video images.
Starting Page	187
Ending Page	198
Page Count	12
File Size	3492101
File Format	PDF
ISSN	00189472
Volume Number	17
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1987-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Data mining Piecewise linear techniques Multidimensional systems Breast tissue X-ray imaging Infrared imaging Process design Error analysis Linear programming
Content Type	Text
Resource Type	Article
Subject	Engineering

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