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Fuzzy-Rough Discriminative Feature Selection and Classification

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Computational Intelligence in Multi-Feature Visual Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 556))

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Abstract

Classification of datasets with multiple features is computationally intensive. Fuzzy-rough set based feature selection and classification requires reduced computational efforts. Lower and upper approximations of fuzzy equivalence classes are useful in finding discriminative features and classification boundaries in a dataset. This chapter discusses a fuzzy-rough single cluster (FRSC) classifier which is a discriminative feature selection and classification algorithm. The FRSC classifier translates each quantitative value of a feature into fuzzy sets of linguistic terms using membership functions and, identifies discriminative features. The membership functions are formed by partitioning the feature space into fuzzy equivalence classes, using feature cluster centers identified through subtractive clustering. Classification rules are generated using fuzzy membership values partitioning the lower and upper approximations. The patterns are classified through a voting process. Both the feature selection and classification algorithms have polynomial time complexity. The algorithm is tested in two types of classification problems, namely, cancer and image-pattern classification. The large number of gene expression profiles and relatively small number of available samples make the feature selection a key step in microarray based cancer classification. The algorithm identified relevant features (predictive genes in the case of cancer data) and provided good classification accuracy, at a less computational cost, with good margin of classification. A comparison of the performance of the FRSC classifier with other relevant classification methods shows the classifier’s better discriminative power.

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Notes

  1. 1.

    The subtractive clustering radius represents the width of the data considered in each step of clustering. A radius within [0.2 0.5] leads to a diameter within [0.4 1.0] and it covers 40–100 % of data width. This is the usual range reported in literature [2]. The number of rules and accuracy decrease with the radius.

  2. 2.

    Refer Appendix-A for an illustration of the formation of fuzzy membership functions, and the calculation of \(\{\mu _{A_{L}}, \mu _{A_{H}}\}\) and \(\{A_L, A_H\}\).

  3. 3.

    The algorithm provides better results when the average value is considered. The other possible values for \(d_{\mu }\) are \(d_{\mu _{A_{L}}}\), \(d_{\mu _{A_{H}}}\) or a weighted average of \(d_{\mu _{A_{L}}}\) and \(d_{\mu _{A_{H}}}\).

  4. 4.

    Voting is positive if the voted class and the actual class are the same. Otherwise it is negative.

  5. 5.

    The list of 55 top ranked genes is available in [9].

  6. 6.

    These genes are listed in the additional material Table S1 of [24].

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Acknowledgments

Figures and tables in this chapter are adapted from Applied Soft Computing, Vol.11, Issue No.4, Pramod Kumar Pisharady, Prahlad Vadakkepat, and Loh Ai Poh, ’Fuzzy-Rough discriminative feature selection and classification algorithm, with application to microarray and image datasets’, Page Nos. 3429–3440, Copyright (2011), with permission from Elsevier.

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  1. Inst. of High Performance Computing, A*STAR, Singapore, Singapore

    Pramod Kumar Pisharady

  2. Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Prahlad Vadakkepat & Loh Ai Poh

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  1. Pramod Kumar Pisharady
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Pisharady, P.K., Vadakkepat, P., Poh, L.A. (2014). Fuzzy-Rough Discriminative Feature Selection and Classification. In: Computational Intelligence in Multi-Feature Visual Pattern Recognition. Studies in Computational Intelligence, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-287-056-8_4

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  • DOI: https://doi.org/10.1007/978-981-287-056-8_4

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