A Novel Non-Dominated Sorting Dragonfly Optimization With Evolutionary Population Dynamics Based Multi-Objective Approach For Feature Selection Problems
Keywords:
Feature selection; Dragonfly optimization; Multi-objective Optimization; Evolutionary Population Dynamics; Non-dominated SortingAbstract
Feature selection is a multi-objective problem which includes two contradictory objectives. It is an effective method in classification to eradicate noise, inappropriate and redundant features to maximize the classification precision and reduce the number of chosen features. In this study, meta-heuristic algorithm with multi-objective approach have been tried to explore feature selection problem with a combination of non-dominated sorting dragonfly algorithm and evolutionary population dynamics strategy. First, to enhance the value of non-dominated solutions, an evolutionary population dynamic strategy is integrated with a heuristic natural selection operators. Second, to avoid the local optimum trap and enrich the population variety, to upgrade the step size and to maintain exploration and exploitation balance, a strategy is planned to optimize these issues. Finally a Pareto optimal solutions are obtained from the non-dominated sorting strategy which makes the algorithm appropriate for handling multi-objective feature selection problems. Simulations are performed on 18 datasets from UCI repository. The proposed NDSDA, NDSDA_EPD and NDSDA_EPD_CM approaches are compared with the existing dragonfly algorithms. The proposed algorithms outperforms the other techniques by enhancing the grouping accuracy and decreasing the preferred features count.
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