Publication Type : Conference Proceedings
Thematic Areas : Wireless Network and Application
Publisher : 2017 International Conference on Networks Advances in Computational Technologies (NetACT).
Source : 2017 International Conference on Networks Advances in Computational Technologies (NetACT) (2017)
Url : https://ieeexplore.ieee.org/document/8076761
Keywords : Balanced classes, Bayes methods, Birds, classification, Classification algorithms, cuckoo birds, Cuckoo search optimization, Feature extraction, Feature selection, imbalanced classes, irrelevant data, learning (artificial intelligence), metaheuristic optimization algorithm, Naive Bayes, Naive Bayes classifier, Niobium, noisy data, optimal feature subset selection, optimisation, Optimization, optimization problem, Parallel algorithms, parallel cuckoo search optimization algorithm, Pattern classification, PCSNB, PCSO, preprocessing step, redundant data, search problems, sociology, Statistics.
Campus : Coimbatore
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Computer Science
Year : 2017
Abstract : 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.
Cite this Research Publication : T. S. Sujana, Madhusudana Rao Nalluri, and R. S. Reddy, “An efficient feature selection using parallel cuckoo search and naïve Bayes classifier”, 2017 International Conference on Networks Advances in Computational Technologies (NetACT). 2017.