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Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

Author

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  • Chen Zhang
  • Zhiwei Ni
  • Liping Ni
  • Na Tang

Abstract

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.

Suggested Citation

  • Chen Zhang & Zhiwei Ni & Liping Ni & Na Tang, 2016. "Feature selection method based on multi-fractal dimension and harmony search algorithm and its application," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(14), pages 3476-3486, October.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:14:p:3476-3486
    DOI: 10.1080/00207721.2015.1086931
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    References listed on IDEAS

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    1. Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
    2. Hasan, Basima Hani F. & Abu Doush, Iyad & Al Maghayreh, Eslam & Alkhateeb, Faisal & Hamdan, Mohammad, 2014. "Hybridizing Harmony Search algorithm with different mutation operators for continuous problems," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 1166-1182.
    3. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
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    Cited by:

    1. Bingtao Zhang & Peng Cao, 2019. "Classification of high dimensional biomedical data based on feature selection using redundant removal," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-19, April.

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