Evolutionary Multitasking for Feature Selection in High-Dimensional Classification via Particle Swarm Optimization

Feature selection (FS) is an important preprocessing technique for improving the quality of feature sets in many practical applications. Particle swarm optimization (PSO) has been widely used for FS due to being efficient and easy to implement. However, when dealing with high-dimensional data, most of the existing PSO-based FS approaches face the problems of falling into local optima and high-computational cost. Evolutionary multitasking is an effective paradigm to enhance global search capability and accelerate convergence by knowledge transfer among related tasks. Inspired by evolutionary multitasking, this article proposes a multitasking PSO approach for high-dimensional FS. The approach converts a high-dimensional FS task into several related low-dimensional FS tasks, then finds an optimal feature subset by knowledge transfer between these low-dimensional FS tasks. Specifically, a novel task generation strategy based on the importance of features is developed, which can generate highly related tasks from a dataset adaptively. In addition, a new knowledge transfer mechanism is presented, which can effectively implement positive knowledge transfer among related tasks. The results demonstrate that the proposed method can evolve a feature subset with higher classification accuracy in a shorter time than other state-of-the-art FS methods on high-dimensional classification.

I. Introduction

Classification is a widely-studied data mining task in academia and industry, which aims to classify unseen data based on the information presented by its features [1]. With the rapid development of emerging techniques, high-dimensional data become common in many real-world applications, such as job shop scheduling [2] and text classification [3]. In such data, many features (i.e., irrelevant and redundant features) are not useful for class prediction, and they not only increase the computational complexity of a learning algorithm but also degrade the classification performance due to “the curse of dimensionality” [4]. Feature selection (FS) is an effective data preprocessing technique, and is capable of solving this challenge by choosing a subset of relevant features from the original features [5].