Optimization of Attribute Selection Model Using Bio-Inspired Algorithms

Authors

  • Mohammad Aizat Basir School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia
  • Yuhanis Yusof School of Computing, Universiti Utara Malaysia, Malaysia
  • Mohamed Saifullah Hussin School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia

DOI:

https://doi.org/10.32890/jict2019.18.1.3

Keywords:

Feature selection, bio-inspired algorithms, data classification, data mining

Abstract

Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis. To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms. Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction.

 

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Published

11-12-2018

Issue

Vol. 18 No. 1 (2019): Journal of Information and Communication Technology (JICT) Vol.18, No.1, January 2019

Section

Articles

How to Cite

Basir, M. A., Yusof, Y., & Hussin, M. S. (2018). Optimization of Attribute Selection Model Using Bio-Inspired Algorithms. Journal of Information and Communication Technology, 18(1), 35-55. https://doi.org/10.32890/jict2019.18.1.3

Research impact

Harvested 2026-09-25
15 citations, from Scopus — the highest of the sources checked

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Identifiers DOI 10.32890/jict2019.18.1.3 OpenAlex W4230824490 Scopus 85058131339

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