A Hybrid Feature Selection Approach Using Artificial Bee Colony and Genetic Algorithm

When data is produced in large quantities, classification requires proper techniques for data analysis and representation. Successful optimization techniques can be used to find the most informative features from the data. Feature selection enhances classification accuracy as it eliminates irrelevant and redundant data from the dataset. Swarm Intelligent algorithms, which are inspired by the social behaviour of living organisms are found to have good performance in feature selection. This paper investigates the possibility of enhancing the artificial bee colony algorithm, one of the efficient swarm intelligent techniques, by hybridizing it with the genetic algorithm. Experiments on various datasets prove that the proposed approach has better results than the existing techniques.