Pre-Processing by a Cost-Sensitive Literal Reduction Algorithm

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TypeArticle
ConferenceLearning, Networks, and Statistics: Proceedings of the 1996 Workshop of the International School for the Synthesis of Expert Knowledge (ISSEK '96)
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
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number41565
NPARC number5750962
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Record identifier762cc364-39a5-4a55-a4c9-4a9b737a2f61
Record created2008-12-02
Record modified2016-05-09
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