Cost-sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm

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ConferenceProceedings of the Seventh International Workshop on Algorithmic Learning Theory (ALT'96), October 1996., Sydney, Australia
Subjecthybrid genetic algorithm
AbstractThis study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in pre-processing before starting the learning process. A case study of data pre-processing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of learning. In addition, cost-sensitive feature elimination can be effective for reducing costs of induced hypotheses.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number40167
NPARC number5751298
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Record identifier8847c59c-20ce-4578-a925-17528be99ba3
Record created2008-12-02
Record modified2016-05-09
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