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Filter-Based Feature Selection Using Two Criterion Functions and Evolutionary Fuzzification

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10053))

Abstract

Real world problems often contain noise features which can decrease effectiveness of classification models. This article proposes a filter-based technique to select a minimal set of features for classification problems. The proposed method employs fuzzification of original features based on irregular-shaped membership functions created by genetic algorithm and particle swarm optimization, and a feature selection process using two criterion functions to evaluate feature subsets. The first function is applied to eliminate features with redundant effects, and the second function is applied to select a feature subset that maximizes inter-class distances and minimize intra-class distances. Standard machine learning data sets in various sizes and complexities are used in experiments. The results show that the proposed technique is effective and performs well in comparisons with other research.

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Authors and Affiliations

  1. Graduate School of Applied Statistics, National Institute of Development Administration, Bangkok, Thailand

    Ohm Sornil

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  1. Ohm Sornil
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Correspondence to Ohm Sornil .

Editor information

Editors and Affiliations

  1. Mahasarakham University , Maha Sarakham, Thailand

    Chattrakul Sombattheera

  2. Harz University of Applied Sciences , Wernigerode, Germany

    Frieder Stolzenburg

  3. University of Science and Technology , Hong Kong, China

    Fangzhen Lin

  4. Macquarie University , North Ryde, New South Wales, Australia

    Abhaya Nayak

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Sornil, O. (2016). Filter-Based Feature Selection Using Two Criterion Functions and Evolutionary Fuzzification. In: Sombattheera, C., Stolzenburg, F., Lin, F., Nayak, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. Lecture Notes in Computer Science(), vol 10053. Springer, Cham. https://doi.org/10.1007/978-3-319-49397-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-49397-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49396-1

  • Online ISBN: 978-3-319-49397-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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