High-order conditional mutual information maximization for dealing with high-order dependencies in feature selection

This paper presents a novel feature selection method based on the conditional mutual information (CMI). The proposed High Order Conditional Mutual Information Maximization (HOCMIM) method 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 pro-cedure, which speeds up the entire feature selection process. The experiments are run on a set of bench-mark datasets (20 in total). The HOCMIM is compared with eighteen state-of-the-art feature selection al-gorithms, 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. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a novel feature selection method based on conditional mutual information. The method incorporates high order dependencies into the feature selection process and speeds up the process through a greedy search procedure. Experimental results show that the proposed method outperforms other algorithms in terms of accuracy and speed.