NDLI: A Constrained Genetic Algorithm for Efficient Dimensionality Reduction for Pattern Classification

Content Provider	IEEE Xplore Digital Library
Author	Panicker, Rajesh Chandrasekhara Puthusserypady, Sadasivan
Copyright Year	2007
Abstract	In automated pattern recognition systems, the two main challenges are feature selection and extraction. The fea- tures selected directly affects the number of measurements required; and extracting low-dimensional features from the selected ones reduces the computational complexity of the classifier. In traditional approaches, human expertise is obligatory for feature selection and statistical techniques are employed for feature projection. In this paper, a con- strained genetic algorithm for performing these two tasks simultaneously, in conjunction with the k-nearest neighbor classifier is proposed. This algorithm requires minimal hu- man intervention as it realizes good tradeoff solutions be- tween classification accuracy, feature measurement require- ments, and computational complexity.
Starting Page	424
Ending Page	427
File Size	600203
Page Count	4
File Format	PDF
ISBN	0769530729
e-ISBN	9780769530727
DOI	10.1109/CIS.2007.193
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern classification Humans Scattering Training data Feature extraction Covariance matrix Pattern recognition Computational complexity Genetic algorithms Principal component analysis
Content Type	Text
Resource Type	Article

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