Please use this identifier to cite or link to this item: https://doi.org/10.1016/0950-7051(95)01030-0
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dc.titleDimensionality reduction via discretization
dc.contributor.authorLiu, H.
dc.contributor.authorSetiono, R.
dc.date.accessioned2014-10-27T06:02:04Z
dc.date.available2014-10-27T06:02:04Z
dc.date.issued1996-02
dc.identifier.citationLiu, H., Setiono, R. (1996-02). Dimensionality reduction via discretization. Knowledge-Based Systems 9 (1) : 67-72. ScholarBank@NUS Repository. https://doi.org/10.1016/0950-7051(95)01030-0
dc.identifier.issn09507051
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/99243
dc.description.abstractThe existence of numeric data and large numbers of records in a database present a challenging task in terms of explicit concepts extraction from the raw data. The paper introduces a method that reduces data vertically and horizontally, keeps the discriminating power of the original data, and paves the way for extracting concepts. The method is based on discretization (vertical reduction) and feature selection (horizontal reduction). The experimental results show that (a) the data can be effectively reduced by the proposed method; (b) the predictive accuracy of a classifier (C4.5) can be improved after data and dimensionality reduction; and (c) the classification rules learned are simpler.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/0950-7051(95)01030-0
dc.sourceScopus
dc.subjectDimensionality reduction
dc.subjectDiscretization
dc.subjectKnowledge discovery
dc.typeArticle
dc.contributor.departmentINFORMATION SYSTEMS & COMPUTER SCIENCE
dc.description.doi10.1016/0950-7051(95)01030-0
dc.description.sourcetitleKnowledge-Based Systems
dc.description.volume9
dc.description.issue1
dc.description.page67-72
dc.description.codenKNSYE
dc.identifier.isiutA1996UU19400006
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