Show simple item record Jensen, Richard Shen, Qiang 2008-01-24T15:02:00Z 2008-01-24T15:02:00Z 2005
dc.identifier.citation Jensen , R & Shen , Q 2005 , ' Fuzzy-Rough Feature Significance for Fuzzy Decision Trees ' pp. 89-96 . en
dc.identifier.other PURE: 75271
dc.identifier.other PURE UUID: 0e18ac30-2ca7-4901-8138-cb197d71f6e0
dc.identifier.other dspace: 2160/476
dc.identifier.other DSpace_20121128.csv: row: 363
dc.identifier.other Scopus: 50249178319
dc.description R. Jensen and Q. Shen, 'Fuzzy-Rough Feature Significance for Fuzzy Decision Trees,' in Proceedings of the 2005 UK Workshop on Computational Intelligence, pp. 89-96, 2005. en
dc.description.abstract Crisp decision trees are one of the most popular classification algorithms in current use within data mining and machine learning. However, although they possess many desirable features, they lack the ability to model vagueness. As a result of this, the induction of fuzzy decision trees (FDTs) has become an area of much interest. One important aspect of tree induction is the choice of feature at each stage of construction. If weak features are selected, the resulting decision tree will be meaningless and will exhibit poor performance. This paper introduces a new measure of feature significance based on fuzzy-rough sets for use within fuzzy ID3. The measure is experimentally compared with leading feature rankers, and is also compared with traditional fuzzy entropy for fuzzy tree induction. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof en
dc.rights en
dc.title Fuzzy-Rough Feature Significance for Fuzzy Decision Trees en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en

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