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Neural Computing and Applications

, Volume 29, Issue 4, pp 925–934 | Cite as

Modified cuckoo search algorithm with rough sets for feature selection

  • Mohamed Abd El AzizEmail author
  • Aboul Ella Hassanien
Original Article

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.

Keywords

Hamming distance Modified cuckoo search (MCS) Feature selection Rough sets Lévy flight 

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Department of Mathematics, Faculty of ScienceZagazig UniversityZagazigEgypt
  2. 2.Faculty of Computers InformationCairo UniversityCairoEgypt

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