Selecting representative examples and attributes by a genetic algorithm

Authors:
Antonin Rozsypal;Miroslav Kubat
Affiliations:
Center for Advanced Computer Studies, University of Louisiana in Lafayette, Lafayette, LA 70504-4330, USA. E-mail: axr8951@cacs.louisiana.edu;Department of Electrical and Computer Egineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33124-0640, USA. E-mail: mkubat@chronos.ece.miami.edu
Venue:
Intelligent Data Analysis
Year:
2003

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Abstract

A nearest-neighbor classifier compares an unclassified object to a set of pre-classified examples and assigns to it the class of the most similar of them (the object's nearest neighbor). In some applications, many pre-classified examples are available and comparing the object to each of them is expensive. This motivates studies of methods to remove redundant and noisy examples. Another strand of research seeks to remove irrelevant attributes that compromise classification accuracy. The paper suggests to use the genetic algorithm to address both issues simultaneously. Experiments indicate considerable reduction of the set of examples, and of the set of attributes, without impaired classification accuracy. The algorithm compares favorably with earlier solutions.