Novel Feature Selection Algorithms Based on Crowding Distance and Pearson Correlation Coefficient

Abstract

Feature Selection is an important phase in classification models. Feature Selection is an effective task used to decrease the dimensionality and eliminate redundant and unrelated features. In this paper, three novel algorithms for feature selection problem are proposed. The first one is a filter method, the second one is a wrapper method, and the last one is a hybrid filter method. Both the proposed algorithms use the crowding distance used in the multiobjective optimization as a new metric to assess the importance of the features. The idea behind the use of the crowding distance is that the less crowded features have great impacts on the target attribute (class), and the crowded features have generally the same impact on the class attribute. To enhance the crowded distance, a combination with other metrics will give good results. In this work, the hybrid method combines between the crowding distance and Pearson correlation coefficient to well order the importance of features. Experiments on well-known benchmark datasets including large microarray datasets have shown the effectiveness and the robustness of the proposed algorithms.