A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

Abstract

Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature selection using the two meta-heuristic based approaches (EO and ACO) were compared with conventional Best First Search (BFS) and Greedy Stepwise (GS) algorithms on news document categorization. The comparative results showed that global optimal feature subsets were attained using adaptive parameters tuning in meta-heuristic based feature selection optimization scheme. In addition, the selected number of feature subsets were minimized dramatically for document classification.