High-Order Conditional Mutual Information Maximization for dealing with High-Order Dependencies in Feature Selection

TL;DR

This paper introduces HOCMIM, a new feature selection method based on high order conditional mutual information, which effectively captures complex dependencies and improves accuracy while being computationally efficient.

Contribution

HOCMIM is a novel high order CMI-based feature selection method with a greedy optimization approach, outperforming existing algorithms in accuracy and speed.

Findings

HOCMIM achieves the best accuracy among tested algorithms.

HOCMIM is faster than comparable high order feature selection methods.

HOCMIM effectively captures high order dependencies in features.

Abstract

This paper presents a novel feature selection method based on the conditional mutual information (CMI). The proposed High Order Conditional Mutual Information Maximization (HOCMIM) incorporates high order dependencies into the feature selection procedure and has a straightforward interpretation due to its bottom-up derivation. The HOCMIM is derived from the CMI's chain expansion and expressed as a maximization optimization problem. The maximization problem is solved using a greedy search procedure, which speeds up the entire feature selection process. The experiments are run on a set of benchmark datasets (20 in total). The HOCMIM is compared with eighteen state-of-the-art feature selection algorithms, from the results of two supervised learning classifiers (Support Vector Machine and K-Nearest Neighbor). The HOCMIM achieves the best results in terms of accuracy and shows to be faster than high order feature selection counterparts.