A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain

被引：21

作者：

Piri, Jayashree ^{[1
]}

Mohapatra, Puspanjali ^{[1
]}

Pradhan, Manas Ranjan ^{[2
]}

Acharya, Biswaranjan ^{[3
]}

Patra, Tapas Kumar ^{[4
]}

机构：

[1] Int Inst Informat Technol, Dept CSE, Bhubaneswar 751029, India

[2] Skyline Univ Coll, Sharjah, U Arab Emirates

[3] Kalinga Inst Ind Technol Deemed Univ, Bhubaneswar 751024, India

[4] Coll Engn & Technol, Bhubaneswar 751029, India

来源：

IEEE ACCESS | 2022年 / 10卷

关键词：

Optimization; Task analysis; Medical diagnostic imaging; Medical services; Filtering theory; Licenses; Filtering algorithms; Chimp optimization; classification; healthcare; data mining; feature selection; multi-objective; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; MUTUAL INFORMATION; CLASSIFICATION; SEARCH; PREDICTION; HARMONY;

D O I：

10.1109/ACCESS.2021.3138403

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Medical datasets frequently include vast feature sets with numerous features that are related to one another. As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. Feature selection (FS) is a major step in classification and also in reducing the dimension. This study attempts a novel Binary Multi-objective Chimp Optimization Algorithm (BMOChOA) with dual archive and k-nearest neighbors (KNN) classifier for mining relevant aspects from medical data. In this research, 12 versions of BMOChOA are implemented based on the group information and types of chaotic functions used. The best Pareto front obtained from suggested BMOChOA variations is compared with three benchmark multi-objective FS methods by taking 14 popular medical datasets of variable dimensions. By analyzing the experimental outputs using four multi-objective performance evaluators, it is found that the proposed FS method is superior in finding the best trade-off between the two objective functions: the number of features and classification performance.

引用

页码：1756 / 1774

页数：19

共 99 条

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