Nonparametric learning of decision regions via the genetic algorithm

A method for nonparametric (distribution-free) learning of complex decision regions in n-dimensional pattern space is introduced. Arbitrary n-dimensional decision regions are approximated by the union of a finite number of basic shapes. The primary examples introduced in this paper are parallelepipe...

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Published inIEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 26; no. 2; pp. 313 - 321
Main Author Yao, L.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.04.1996
Subjects
Artificial neural networks
Biological cells
Ellipsoids
Genetic algorithms
Hypercubes
Least mean square algorithms
Least squares approximation
Parameter estimation
Shape
Training data
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Summary:A method for nonparametric (distribution-free) learning of complex decision regions in n-dimensional pattern space is introduced. Arbitrary n-dimensional decision regions are approximated by the union of a finite number of basic shapes. The primary examples introduced in this paper are parallelepipeds and ellipsoids. By explicitly parameterizing these shapes, the decision region can be determined by estimating the parameters associated with each shape. A structural random search type algorithm called the genetic algorithm is applied to estimate these parameters. Two complex decision regions are examined in detail. One is linearly inseparable, nonconvex and disconnected. The other one is linearly inseparable, nonconvex and connected. The scheme is highly resilient to misclassification errors. The number of parameters to be estimated only grows linearly with the dimension of the pattern space for simple version of the scheme.
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ISSN:1083-4419
1941-0492
DOI:10.1109/3477.485882