期刊名称：Advanced Computing : an International Journal
印刷版ISSN：2229-726X
电子版ISSN：2229-6727
出版年度：2019
卷号：10
期号：1/2/3/4/5/
页码：1-10
DOI：10.5121/acij.2019.10501
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：A well-constructed classification model highly depends on input feature subsets from a dataset, which may
contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical
datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select
the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find
optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine
Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature
selection from the medical data. In these algorithms, the search space remains continuous, while a binary
position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The
proposed algorithms are compared with four latest binary optimization algorithms over five medical
datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance
the accuracy of classification on these medical datasets compared to four other algorithms.
关键词：Medical data; Feature selection; metaheuristic algorithm; Sine Cosine Algorithm; Transfer function;
