An efficient feature selection using parallel cuckoo search and naïve Bayes classifier

In real world, the datasets are having varying dimensions which incorporates noisy, irrelevant and redundant data which is hard to analyze. Feature selection is a preprocessing step used for selecting the significant information. The selection of optimal feature subset is an optimization problem which has been solved by several versions of metaheuristic algorithms. The metaheuristic optimization algorithm based on the behavior of cuckoo birds is adapted to build the parallel cuckoo search optimization (PCSO) algorithm. The wrapper approach of parallel cuckoo search with Naive Bayes (PCSNB) is developed by combining the power of exploration of PCSO with the speed of Naïve Bayes (NB) classifier for finding feature subset that maximizes the accuracy. The proposed approach is tested on seven different datasets which are having balanced and imbalanced classes and contrasted with other metaheuristic algorithms. The results are showing higher prediction accuracy than other algorithms and selects the feature subset with less features.