Abstract
Feature selection is a complex pre-processing step in data mining that enhances classification accuracy by selecting the minimum number of relevant features. Artificial bee colony algorithm (ABC) is one of the successful swarm intelligent algorithms for feature selection, image processing, data analytics, protein structure prediction, etc. It simulates the honey foraging behavior of the bee swarm. But it tends to low convergence speed and local optima stagnation. Hybrid meta-heuristics can enhance the performance of existing swarm algorithms. This paper proposes a hybrid approach for the ABC algorithm by incorporating genetic operators into it. The mutation operator is used to explore the better-quality neighborhood while the crossover is used to enhance the quality of solutions by implementing diversity into them. The performance of the proposed method is evaluated using UCI data sets and compared with existing swarm algorithms for feature selection. The effectiveness of the proposed method is evident from the results.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Swarm Intelligence Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
