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A New Fuzzy-Rough Hybrid Merit to Feature Selection

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Transactions on Rough Sets XX

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 10020))

Abstract

Feature selecting is considered as one of the most important pre-process methods in machine learning, data mining and bioinformatics. By applying pre-process techniques, we can defy the curse of dimensionality by reducing computational and storage costs, facilitate data understanding and visualization, and diminish training and testing times, leading to overall performance improvement, especially when dealing with large datasets. Correlation feature selection method uses a conventional merit to evaluate different feature subsets. In this paper, we propose a new merit by adapting and employing of correlation feature selection in conjunction with fuzzy-rough feature selection, to improve the effectiveness and quality of the conventional methods. It also outperforms the newly introduced gradient boosted feature selection, by selecting more relevant and less redundant features. The two-step experimental results show the applicability and efficiency of our proposed method over some well known and mostly used datasets, as well as newly introduced ones, especially from the UCI collection with various sizes from small to large numbers of features and samples.

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Acknowledgments

This work has been partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Research & Development Corporation of Newfoundland and Labrador (RDC).

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Authors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, St. John’s, Nl, A1B 3X5, Canada

    Javad Rahimipour Anaraki

  2. Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Nl, A1B 3V6, Canada

    Saeed Samet

  3. Department of Computer Science, Memorial University of Newfoundland, St. John’s, Nl, A1B 3X5, Canada

    Wolfgang Banzhaf

  4. Department of Computer Engineering, Shahid Bahonar University of Kerman, 7616914111, Kerman, Iran

    Mahdi Eftekhari

Authors
  1. Javad Rahimipour Anaraki
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  2. Saeed Samet
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  3. Wolfgang Banzhaf
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  4. Mahdi Eftekhari
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Corresponding author

Correspondence to Javad Rahimipour Anaraki .

Editor information

Editors and Affiliations

  1. University of Manitoba , Winnipeg, Manitoba, Canada

    James F. Peters

  2. University of Warsaw , Warsaw, Poland

    Andrzej Skowron

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Anaraki, J.R., Samet, S., Banzhaf, W., Eftekhari, M. (2016). A New Fuzzy-Rough Hybrid Merit to Feature Selection. In: Peters, J., Skowron, A. (eds) Transactions on Rough Sets XX. Lecture Notes in Computer Science(), vol 10020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53611-7_1

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  • DOI: https://doi.org/10.1007/978-3-662-53611-7_1

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