Feature Selection Method Based On GWO-PSO for Coronary Artery Disease Classification
Eluri Rama Krishna, Nagaraju Devarakonda
Abstract
In this analysis, Optimization of Hybrid Grey Wolf technique for Feature Selection analysis is implemented. In many areas standard performance is given by Grey Wolf Optimizer's (GWO). To enhance the performance different GWO factors are signified GWO mainly based on the two conflicting concepts. This will exploit the optimal solutions. The performance of search algorithms is improved by balancing the both exploration and exploitation. For continuous search space problems are obtained from the original hybrid approach which is superior to performance. The main binary problem is fromfeature selection. Hence hybrid PSOGWO is also known as BGWOPSO. This is the best feature subset. To produce the best results, Euclidean separation matrix and K-Nearest Neighbor's classifier are used. Performance is evaluated using one common benchmark dataset from the UCI repository. According to the findings, the whale optimization algorithm, binary genetic algorithm, binary PSO, binary genetic algorithm, and Binary GWO (BGWO) provide the highest levels of performance in terms of accuracy, the selection of the most effective optimal features, and the amount of time required to compute.