Binary Sine Cosine Algorithms for Feature Selection from Medical Data

TL;DR

This paper introduces two novel binary metaheuristic algorithms, SBSCA and VBSCA, for feature selection in medical datasets, demonstrating improved classification accuracy over existing methods.

Contribution

The paper proposes two new binary sine cosine algorithms utilizing transfer functions for effective feature selection in medical data analysis.

Findings

Both algorithms outperform four recent binary optimization methods.

The algorithms improve classification accuracy on five UCI medical datasets.

Experimental results validate the effectiveness of the proposed methods.

Abstract

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.