Feature selection based on chaotic binary black hole algorithm for data classification

被引：39

作者：

Qasim, Omar Saber ^{[1
]}

Al-Thanoon, Niam Abdulmunim ^{[2
]}

Algamal, Zakariya Yahya ^{[3
]}

机构：

[1] Univ Mosul, Dept Math, Mosul, Iraq

[2] Univ Mosul, Dept Operat Res & Intelligent Tech, Mosul, Iraq

[3] Univ Mosul, Dept Stat & Informat, Mosul, Iraq

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2020年 / 204卷

关键词：

Black hole algorithm; Chaotic map; Feature selection; Chemical model classification; OPTIMIZATION;

D O I：

10.1016/j.chemolab.2020.104104

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the advance of generating high-dimensional data, feature selection is the most significant procedure to guarantee selecting the most discriminative subset of features and to improve the classification performance. As a result, a binary black hole optimization algorithm (CBBHA) has been developed by getting inspired from natural phenomena. In this paper, the most discriminating features are selected by a new chaotic binary black hole algorithm (CBBHA) where chaotic maps embedded with movement of stars in the BBHA. Ten chaotic maps are employed. Experiments on three chemical datasets show the proposed algorithm, CBBHA, has an advantage over the standard BBHA in terms of selecting relevant features with a high classification performance. Additionally the performance of CBBHA is compared with BBHA in term of the computational time efficiency which is revealing that CBBHA outperforms the BBHA.

引用

页数：6

共 35 条

[1] Tuning parameter estimation in SCAD-support vector machine using firefly algorithm with application in gene selection and cancer classification [J].