A Hybrid Feature Selection Method for Classification Purposes

This paper presents a novel combination of filter features selection algorithms for classification problem. Feature selection is one of the most important issues in pattern recognition, machine learning and computer vision. The main objective of feature selection regards the dimensionality reduction, the performance of machine learning improvement and the process comprehensibility increase. Exhaustive search method is the only method which guarantees to find the optimal subsets but its computational time complexity is exponential. In this paper the set of available variables are firstly reduced using a combination of filter selection methods and then exhaustive search is performed in order to obtain a sub-optimal set of variables in a reasonable time. The proposed approach is tested on several commonly used datasets from UCI repository and two datasets coming from industrial context.