A Significant Feature Selection in the Mahalanobis Taguchi System using Modified-Bees Algorithm


This paper compares the performance of orthogonal array (OA), modified-Bees Algorithm (mBA) and conventional Bees Algorithm (BA) in significant feature selection scheme (optimization) of the Mahalanobis-Taguchi System (MTS) methodology. The main contribution of this work is to address both performances in terms of computing cost i.e. computing time as well as classification accuracy rate. Several studies have been conducted to evaluate the performance of OA against other heuristic search techniques in MTS methodology however, discussions in terms of the computing speed performances were found to be lacking. Instead, the accuracy performances were given the emphasis by drawing criticisms towards the deployment of OA as ineffective as compared to other state-of-the-art heuristic algorithms. Bees Algorithm (BA) is one heuristic search technique that discovers optimal (or near optimal) solutions using search strategy mimics the social behaviour of a honeybee colony. In this comparison work, modified-BA (mBA) is introduced into the optimization scheme of MTS with a modification on its neighbourhood search mechanism from the original BA. Instead of searching in random mode, a backward selection method is proposed. MD is used as the result assessment metric while the larger-the-better type of SNR is deployed as the algorithm's objective function. The historical heart liver disease data are used as the case study on which the comparisons between OA, mBA and BA performances specifically in terms of the computing speed are made and addressed. The outcomes showed a promising performance of the mBA as compared to OA with a comparable classification accuracy rate. Eventhough OA outperformed mBA in terms of computational speed, the MTS manage to classify at the expense of lower number of variables suggested by mBA. The mBA also converges faster than the conventional BA in finding the potential solution of the case problem.


Engineering Management and Systems Engineering


This work was supported under the Fundamental Research Grant Scheme (FRGS) awarded by the Ministry of Education, Malaysia (Ref: FRGS/1/2019/TK03/UTM/02/10) and Universiti Teknologi Malaysia.

Keywords and Phrases

Bees Algorithm; Feature Selection; Mahalanobis-Taguchi System; Orthogonal Array; Swarm Intelligence

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Article - Journal

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Publication Date

01 Jan 2020