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Fuzzy clustering-based discretization for gene expression classification

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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 c-means clustering algorithm is employed to transform the training set, particularly quantitative attributes, to a format appropriate for association rule mining. A hill-climbing 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 rule-based 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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Authors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Keivan Kianmehr, Mohammed Alshalalfa & Reda Alhajj

  2. Department of Computer Science, Global University, Beirut, Lebanon

    Reda Alhajj

Authors
  1. Keivan Kianmehr
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  2. Mohammed Alshalalfa
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  3. Reda Alhajj
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Correspondence to Reda Alhajj.

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Kianmehr, K., Alshalalfa, M. & Alhajj, R. Fuzzy clustering-based discretization for gene expression classification. Knowl Inf Syst 24, 441–465 (2010). https://doi.org/10.1007/s10115-009-0214-2

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  • DOI: https://doi.org/10.1007/s10115-009-0214-2

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