A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

Ant colony optimization (ACO) is a metaheuristic approach inspired from the behaviour of natural ants and can be used to solve a variety of combinatorial optimization problems. Classification rule induction is one of the problems solved by the Ant-miner algorithm, a variant of ACO, which was initiat...

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Main Author:	Rizauddin, Saian
Format:	Thesis
Language:	eng eng
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	https://etd.uum.edu.my/3289/1/RIZAUDDIN_SAIAN.pdf https://etd.uum.edu.my/3289/2/RIZAUDDIN_SAIAN_13.pdf
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id	my-uum-etd.3289
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ku-Mahamud, Ku Ruhana
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Rizauddin, Saian A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
description	Ant colony optimization (ACO) is a metaheuristic approach inspired from the behaviour of natural ants and can be used to solve a variety of combinatorial optimization problems. Classification rule induction is one of the problems solved by the Ant-miner algorithm, a variant of ACO, which was initiated by Parpinelli in 2001. Previous studies have shown that ACO is a promising machine learning technique to generate classification rules. However, the Ant-miner is less class focused since the rule’s class is assigned after the rule was constructed. There is also the case where the Ant-miner cannot find any optimal solution for some data sets. Thus, this thesis proposed two variants of hybrid ACO with simulated annealing (SA) algorithm for solving problem of classification rule induction. In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. Benchmark data sets from various fields were used to test the proposed algorithms. Experimental results obtained from this proposed algorithm are comparable to the results of the Ant-miner and other well-known rule induction algorithms in terms of rule accuracy, but are better in terms of rule simplicity. The second proposed algorithm uses SA to optimize the terms selection while constructing a rule. The algorithm fixes the class before rule's construction. Since the algorithm fixed the class before each rule's construction, a much simpler heuristic and fitness function is proposed. Experimental results obtained from the proposed algorithm are much higher than other compared algorithms, in terms of predictive accuracy. The successful work on hybridization of ACO and SA algorithms has led to the improved learning ability of ACO for classification. Thus, a higher predictive power classification model for various fields could be generated.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Rizauddin, Saian
author_facet	Rizauddin, Saian
author_sort	Rizauddin, Saian
title	A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
title_short	A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
title_full	A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
title_fullStr	A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
title_full_unstemmed	A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
title_sort	hybrid of ant colony optimization algorithm and simulated annealing for classification rules
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2013
url	https://etd.uum.edu.my/3289/1/RIZAUDDIN_SAIAN.pdf https://etd.uum.edu.my/3289/2/RIZAUDDIN_SAIAN_13.pdf
_version_	1776103615799230464
spelling	my-uum-etd.32892023-02-08T02:28:47Z A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules 2013 Rizauddin, Saian Ku-Mahamud, Ku Ruhana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA75 Electronic computers. Computer science Ant colony optimization (ACO) is a metaheuristic approach inspired from the behaviour of natural ants and can be used to solve a variety of combinatorial optimization problems. Classification rule induction is one of the problems solved by the Ant-miner algorithm, a variant of ACO, which was initiated by Parpinelli in 2001. Previous studies have shown that ACO is a promising machine learning technique to generate classification rules. However, the Ant-miner is less class focused since the rule’s class is assigned after the rule was constructed. There is also the case where the Ant-miner cannot find any optimal solution for some data sets. Thus, this thesis proposed two variants of hybrid ACO with simulated annealing (SA) algorithm for solving problem of classification rule induction. In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. Benchmark data sets from various fields were used to test the proposed algorithms. Experimental results obtained from this proposed algorithm are comparable to the results of the Ant-miner and other well-known rule induction algorithms in terms of rule accuracy, but are better in terms of rule simplicity. The second proposed algorithm uses SA to optimize the terms selection while constructing a rule. The algorithm fixes the class before rule's construction. Since the algorithm fixed the class before each rule's construction, a much simpler heuristic and fitness function is proposed. Experimental results obtained from the proposed algorithm are much higher than other compared algorithms, in terms of predictive accuracy. The successful work on hybridization of ACO and SA algorithms has led to the improved learning ability of ACO for classification. Thus, a higher predictive power classification model for various fields could be generated. 2013 Thesis https://etd.uum.edu.my/3289/ https://etd.uum.edu.my/3289/1/RIZAUDDIN_SAIAN.pdf text eng public https://etd.uum.edu.my/3289/2/RIZAUDDIN_SAIAN_13.pdf text eng public http://sierra.uum.edu.my/record=b1242349~S1 Ph.D. doctoral Universiti Utara Malaysia Abramson,D., Krishnamoorthy,M., & Dang,H. (1999). Simulated Annealing Cooling Schedules For The School Timetabling Problem. Asia-Pacific Journal of Operational Research, 16, 1–22. Anghinolfi,D., & Paolucci,M. (2008). Simulated Annealing as an Intensification Component in Hybrid Population-based Metaheuristics. In M.T.Cher (Ed.), Simulated Annealing. InTech. Asuncion,A., & Newman,D.J. (2007). UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences. 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A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

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