Discretization and Feature Selection Based on Bias Corrected Mutual Information Considering High-Order Dependencies

ABSTRACT: Mutual Information (MI) based feature selection methods are popular due to their ability to capture the nonlinear relationship among variables. However, existing works rarely address the error (bias) that occurs due to the use of finite samples during the estimation of MI. To the best of our knowledge, none of the existing methods address the bias issue for the high-order interaction term which is essential for better approximation of joint MI. In this paper, we first calculate the amount of bias of this term. Moreover, to select features using Electronic supplementary material The online version of this chapter (10.1007/978-3-030-47426-3_64) contains supplementary material, which is available to authorized users.

SUBMITTER: Lauw H 

PROVIDER: S-EPMC7206174 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature