Discriminative feature selection with directional outliers correcting for data classification

A B S T R A C T With the rapid development of multimedia technologies (e.g. deep learning), Feature Selection (FS) is now playing a critical role in acquiring discriminative features from massive data. Traditional FS methods score feature importance and select the top best features by treating all instances equally; Hence, valuable instances like directional outliers (DOs), which are specific outliers closer to other class centres than to their owns, seldom receive particular attention during feature selection. Based on our observation, DOs derive from misclassified instances which lead to misclassification. In this paper, we present a novel supervised feature selection method entitled Feature Selection via Directional Outliers Correcting (FSDOC), for accurate data classification. The proposed FSDOC includes an optimization algorithm to capture DOs, and two correcting algorithms to reasonably capture redundant features by correcting DOs with intraclass deviation minimization and interclass relative distance maximization. We give theoretical guarantees and adequate analysis on all algorithms to show the effectiveness of FSDOC. Extensive experiments on fifteen public datasets, and two case studies of deep features and very-high dimensional Fisher Vector selection, demonstrate the superior performance of FSDOC. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

In this paper, a novel supervised feature selection method called FSDOC is proposed for accurate data classification. FSDOC includes an optimization algorithm to capture directional outliers and two correcting algorithms to capture redundant features. The effectiveness of FSDOC is demonstrated through theoretical guarantees, analysis, and extensive experiments.