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Title of Journal: Knowl Inf Syst

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Abbravation: Knowledge and Information Systems

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Springer-Verlag

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DOI

10.1016/0005-7967(79)90114-1

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0219-3116

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Fuzzy clusteringbased discretization for gene exp

Authors: Keivan Kianmehr Mohammed Alshalalfa Reda Alhajj
Publish Date: 2009/06/03
Volume: 24, Issue: 3, Pages: 441-465
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Abstract

This paper presents a novel classification approach that integrates fuzzy class association rules and support vector machines A fuzzy discretization technique based on fuzzy cmeans clustering algorithm is employed to transform the training set particularly quantitative attributes to a format appropriate for association rule mining A hillclimbing procedure is adapted for automatic thresholds adjustment and fuzzy class association rules are mined accordingly The compatibility between the generated rules and fuzzy patterns is considered to construct a set of feature vectors which are used to generate a classifier The reported test results show that compatibility rulebased feature vectors present a highly qualified source of discrimination knowledge that can substantially impact the prediction power of the final classifier In order to evaluate the applicability of the proposed method to a variety of domains it is also utilized for the popular task of gene expression classification Further we show how this method provide biologists with an accurate and more understandable classifier model compared to other machine learning techniques

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