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International Conference on Discovery Science

DS 2017: Discovery Science pp 155–170Cite as

Improving Classification Accuracy by Means of the Sliding Window Method in Consistency-Based Feature Selection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10558))

Abstract

In the digital era, collecting relevant information of a technological process has become increasingly cheaper and easier. However, due to the huge available amount of data, supervised classification is one of the most challenging tasks within the artificial intelligence field. Feature selection solves this problem by removing irrelevant and redundant features from data. In this paper we propose a new feature selection algorithm called Swcfs, which works well in high-dimensional and noisy data. Swcfs can detect noisy features by leveraging the sliding window method over the set of consecutive features ranked according to their non-linear correlation with the class feature. The metric Swcfs uses to evaluate sets of features, with respect to their relevance to the class label, is the bayesian risk, which represents the theoretical upper error bound of deterministic classification. Experiments reveal Swcfs is more accurate than most of the state-of-the-art feature selection algorithms.

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Authors and Affiliations

  1. Graduate School of Applied Informatics, University of Hyogo, Kobe, Hyogo, Japan

    Adrian Pino Angulo & Kilho Shin

Authors
  1. Adrian Pino Angulo
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  2. Kilho Shin
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Corresponding author

Correspondence to Adrian Pino Angulo .

Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Akihiro Yamamoto

  2. Hokkaido University, Sapporo, Japan

    Takuya Kida

  3. National Institute of Informatics, Tokyo, Japan

    Takeaki Uno

  4. Gakushuin University, Tokyo, Japan

    Tetsuji Kuboyama

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Pino Angulo, A., Shin, K. (2017). Improving Classification Accuracy by Means of the Sliding Window Method in Consistency-Based Feature Selection. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-67786-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67785-9

  • Online ISBN: 978-3-319-67786-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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