A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data

Jia Yun-Tao,Zhang Wan-Qiu,He Chun-Lin,Shi Cheng

doi:10.1155/2021/8462493

Abstract

For high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm optimization algorithm (IBSO-C). In the first phase, an importance-guided feature clustering method is proposed to group similar features, so that the search space in the second phase can be reduced obviously. The second phase applies oneself to finding optimal feature subset by using an improved integer brain storm optimization. Moreover, a new encoding strategy and a time-varying integer update method for individuals are proposed to improve the search performance of brain storm optimization in the second phase. Since the number of feature clusters is far smaller than the size of original features, IBSO-C can find an optimal feature subset fast. Compared with several existing algorithms on some real-world datasets, experimental results show that IBSO-C can find feature subset with high classification accuracy at less computation cost.

Highlights

Feature selection (FS), as an important dimension reduction method, has been applied in various real problems, such as image processing and text classification [1, 2]
Many Swarm intelligence (SI)-based algorithms have been applied in FS problems, such as particle swarm optimization [7,8,9,10,11,12], differential evolution [13,14,15], artificial bee colony algorithm [16,17,18], firefly algorithm [19, 20], salp swarm algorithm [21], ant colony optimization [6], and whale optimization algorithm [22]
Papa et al introduced an improved binary Brain storm optimization (BSO)-based FS algorithm, where a realvalued solution is mapped onto a Boolean hyper cube by using different transfer functions [39]. ese methods above all enhance the capability of BSO on solving FS problems

Summary

Introduction

Feature selection (FS), as an important dimension reduction method, has been applied in various real problems, such as image processing and text classification [1, 2]. Zhang et al applied BSO in FS problems for the first time and proposed a continuous BSO-based FS algorithm (CBSO) [34] They have developed an improved discrete BSO [35], where two new idea clustering and new idea updating mechanisms were proposed to improve the performance of BSO. Combining the Fuzzy Min-Max neural network and BSO to undertake feature selection and classification problems, Pourpanan et al developed a hybrid BSO-based FS method [37]. For high-dimensional data, this paper develops a new evolutionary feature selection algorithm, called the clustering-guided integer BSO algorithm (IBSO-C). (1) e paper proposes a new two-phase hybrid evolutionary FS framework, which effectively combines the capability of fast dimensionality reduction of clustering-based method with the global search ability of evolutionary algorithm. (3) e paper proposes an improved integer BSO (IBSO) for feature selection problems.

Related Work

The Proposed IBSO-C Algorithm

Findings

10 GFE01 IBSO-S IBSO-C