TELKOMNIKA Telecommunication, Computing, Electronics and Control
Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem
Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem
Subject
Bio-inspired, Classification, ENORA, Feature selection, NSGA-II
Description
Optimal selection of features is very difficult and crucial to achieve,
particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
Creator
Mohammad Aizat Basir, Mohamed Saifullah Hussin, Yuhanis Yusof
Source
DOI: 10.12928/TELKOMNIKA.v18i5.15141
Publisher
Universitas Ahmad Dahlan
Date
October 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Mohammad Aizat Basir, Mohamed Saifullah Hussin, Yuhanis Yusof, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem,” Repository Horizon University Indonesia, accessed March 10, 2025, https://repository.horizon.ac.id/items/show/4082.
Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem,” Repository Horizon University Indonesia, accessed March 10, 2025, https://repository.horizon.ac.id/items/show/4082.