An Adaptive Neighborhood Partition Full Conditional Mutual Information Maximization Method for Feature Selection

Abstract

Feature selection is used to eliminate redundant features and keep relevant features, it can enhance machine learning algorithm's performance and accelerate computing speed. In various methods, mutual information has attracted increasingly more attention as it's an effective criterion to measure variable correlation. However, current works mainly focus on maximizing the feature relevancy with class label and minimizing the feature redundancy within selected features, we reckon that pursuing feature redundancy minimization is reasonable but not necessary because part of so-called redundant features also carries some useful information to promote performance. In terms of mutual information calculation, it may distort the true relationship between two variables without proper neighborhood partition. Traditional methods usually split the continuous variables into several intervals even ignore such influence. We theoretically prove how variable fluctuation negatively influences mutual information calculation. To remove the referred obstacles, for feature selection method, we propose a full conditional mutual information maximization method (FCMIM) which only considers the feature relevancy in two aspects. For obtaining a better partition effect and eliminating the negative influence of attribute fluctuation, we put up an adaptive neighborhood partition algorithm (ANP) with the feedback of mutual information maximization algorithm, the backpropagation process helps search for a proper neighborhood partition parameter. We compare our method with several mutual information methods on 17 benchmark datasets. Results of FCMIM are better than other methods based on different classifiers. Results show that ANP indeed promotes nearly all the mutual information methods' performance.