Logarithmic Division for Green Feature Selection: an Information-Theoretic Approach

Abstract

[Abstract]: Feature selection is a popular preprocessing step to reduce the dimensionality of the data while preserving the important information. In this paper we propose an efficient and green feature selection method based on information theory, with the novelty of using the logarithmic division and resort to fixed-point precision. The results of experiments conducted on several datasets indicate the potential of our proposal, as it does not incur in significant information loss compared to the standard method, both in the features selected and in the subsequent classification step. This finding opens up possibilities for a new family of green feature selection methods, which would help to minimize energy consumption and carbon emissions.