Empirical Analysis of Filter Feature Selection Criteria on Financial Datasets

High dimensionality is one of the data quality problems that affects the performance of machine learning models. Feature selection which aims to identify and remove as many redundant and irrelevant features as possible allows to boot the overall performance of the models while reducing the computational cost. However, the choice of an appropriate feature selection method is still a big challenge as there is no the best selection criterion that fits to all datasets. It is then essential to comparatively analyze the performance of feature selection criteria according to different characteristics of high-dimensional datasets, particularly large financial datasets where features are highly-correlated and redundant. In this paper, we explore nine different feature selection criteria which are typically categorized into two classes: (i) information theoretical based criteria and (ii) similarity based criteria over seven public financial datasets. To the best of our knowledge, no previous comprehensive empirical investigation has been carried out to demonstrate the positive effects of feature selection criteria on financial data. Experimental results indicate that the information theoretical-based methods suffer from a high computation time in case of high dimensional data (i.e., high number of features) while the similarity-based methods require significant computations to deal with high volume dataset (i.e., high number of samples).