Hybrid Meta-heuristic Pattern Classification

Feature selection is vital in the field of pattern classification due to accuracy and processing time considerations.The selection of proper features is of greater importance when the initial feature set is considerably large. Text classification is a typical example of this situation, where the size of the initial feature set may reach to hundreds or even thousands. There are numerous research studies in the literature offering different feature selection strategies for text classification, mostly focused on filters. In spite of the extensive number of these studies, there is no significant work investigating the efficacy of a combination of features, which are selected by different selection methods, under different conditions. Proposed algorithm a new hybrid metaheuristic approach for feature selection (ACOFS) has been presented that utilizes ant colony optimization. The main focus of this algorithm is to generate subsets of salient features of reduced size. ACOFS utilizes a hybrid search technique that combines the wrapper and filter approaches. In this regard, ACOFS modifies the standard pheromone update and heuristic information measurement rules based on the above two approaches.