Fast and Efficient Feature Selection Method Using Bivariate Copulas

Handling datasets nowadays has become a crucial task, since today's world is heavily dependent on data information. However, many data tend to be big and contain redundancy which makes them difficult to deal with. Due to that, data pre-processing became almost necessary before using any data, and one of the main tasks in data preprocessing is dimensionality reduction. In this paper we propose a new approach for dimensionality reduction using feature selection method based on bivariate copulas. This approach is a direct application of copulas to describe and model the inter-correlation between any two dimensions bivariate analysis. The study will first show how we use the bivariate method to detect redundant dimensions and eliminate them, and then compare the quality of the results against most-known selection methods in term of accuracy, using statistical precision and classification models.