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Modified cuckoo search algorithm with rough sets for feature selection

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Abstract

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed Abd El Aziz

  2. Faculty of Computers Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

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  1. Mohamed Abd El Aziz
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  2. Aboul Ella Hassanien
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Correspondence to Mohamed Abd El Aziz.

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Aziz, M.A.E., Hassanien, A.E. Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput & Applic 29, 925–934 (2018). https://doi.org/10.1007/s00521-016-2473-7

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