Fractional Calculus-Based Slime Mould Algorithm for Feature Selection Using Rough Set

Rehab Ali Ibrahim,Ibrahim Attiya,Mohamed Abd Elaziz,Dalia Yousri,Samah Alshathri

doi:10.1109/access.2021.3111121

Abstract

Features Selection (FS) techniques have been applied to several real-world applications which contain high dimension data. These FS techniques have main objectives that aim to achieve them, such as removing irrelevant features and increasing classification accuracy. This is considered a bi-objectives optimization problem that requires a suitable technique that can balance between the objectives. So, different sets of FS techniques have been developed, and those techniques that depend on meta-heuristic (MH) established their performance overall traditional FS techniques. However, these MH approaches still require more enhancement to neutralize their exploration and exploitation abilities during the searching process. Enhancing the meta-heuristic optimization algorithm using the perspective of fractional calculus (FC) is an attractive and novel approach. In this paper, the slime mould algorithm (SMA) is modified using the FC for handling the optimizer drawback of the inefficient diversification phase. As a result, a fractional-order SMA is proposed to avoid the local solutions and discover the search landscape efficiently via considering a historic memorize of agents’ positions. The proposed FOSMA is applied to extract features from a set of real-world data and increase classification accuracy. For boosting the optimizer performance while processing with these datasets, the rough set (RS) is used as the fitness function to handle the uncertainty inside the real-world data. Finally, the proposed FOSMA’s results are compared with a set of well-known FS techniques to investigate its performance. The comparison illustrates the superiority of FOSMA in providing high accuracy.

Highlights

The high advancement achieved in information and communication devices and technologies had led to an increase in the dimension of the collection and the stored data at an exponential rate
It can be noticed the characteristics of the tested datasets which collected from different fields and has a different number of features and instances
In this paper, a modified version of the Slime Mould Algorithm (SMA) has been developed as a feature selection method. This modification has been performed using the fractional calculus (FC) and Rough set. Each of these techniques has its task to improve Slime mould algorithm (SMA), such as FC has been applied to enhance the searchability of the agents of the classical SMA during the search process through counting several terms from memory based on FC perspective

Summary

INTRODUCTION

The high advancement achieved in information and communication devices and technologies had led to an increase in the dimension of the collection and the stored data at an exponential rate. Shukla et al [25] proposed a hybrid metaheuristics approach for cancer type classification, and diao et al [26] used modified PSO for identifying or detecting and expectation of the existence and usefulness of pipelines leakage This technique depended on sufficient processing of signals that has acoustic emission and enhanced the variational mode decomposition (VMD) for signal denoising using the particle swarm optimization (PSO) algorithm. For choosing the career of the students, the authors proposed a hybridized distance measure under a picture fuzzy environment where the evaluating information regarding students, subjects, and student’s features are given in picture fuzzy numbers They introduced two types of hybridization approaches that are the hybridization of Hausdorff and Hamming distance measures and the hybridization of Hausdorff and Euclidean distance measures.

BACKGROUND

ROUGH SETS

FRACTIONAL-ORDER SLIME MOULD ALGORITHM

3) EVALUATION PHASE

EXPERIMENTAL RESULTS AND ANALYSIS

CONCLUSION