An adaptive ant colony optimization algorithm for rule-based classification

Classification is an important data mining task with different applications in many fields. Various classification algorithms have been developed to produce classification models with high accuracy. Differing from other complex and difficult classification models, rules-based classification algorith...

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Main Author:	Al-Behadili, Hayder Naser Khraibet
Format:	Thesis
Language:	eng eng eng
Published:	2020
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/8786/1/Deposit%20Permission_s901983.pdf https://etd.uum.edu.my/8786/2/s901983_01.pdf https://etd.uum.edu.my/8786/3/s901983_references.docx
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spelling	my-uum-etd.87862021-11-07T02:37:56Z An adaptive ant colony optimization algorithm for rule-based classification 2020 Al-Behadili, Hayder Naser Khraibet Ku Mahamud, Ku Ruhana Allwawi, Rafid sagban Abbood Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA Mathematics Classification is an important data mining task with different applications in many fields. Various classification algorithms have been developed to produce classification models with high accuracy. Differing from other complex and difficult classification models, rules-based classification algorithms produce models which are understandable for users. Ant-Miner is a variant of ant colony optimisation and a prominent intelligent algorithm widely use in rules-based classification. However, the Ant-Miner has overfitting and easily falls into local optima problems which resulted in low classification accuracy and complex classification rules. In this study, a new Ant-Miner classifier is developed, named Adaptive Genetic Iterated-AntMiner (AGI-AntMiner) that aims to avoid local optima and overfitting problems. The components of AGI-AntMiner includes: i) an Adaptive AntMiner which is a prepruning technique to dynamically select the appropriate threshold based on the quality of the rules; ii) Genetic AntMiner that improves the post-pruning by adding/removing terms in a dual manner; and, iii) an Iterated Local Search-AntMiner that improves exploitation based on multiple-neighbourhood structure. The proposed AGI-AntMiner algorithm is evaluated on 16 benchmark datasets of medical, financial, gaming and social domains obtained from the University California Irvine repository. The algorithm’s performance was compared with other variants of Ant-Miner and state-of-the-art rules-based classification algorithms based on classification accuracy and model complexity. Experimental results proved that the proposed AGI-AntMiner algorithm is superior in two (2) aspects. Hybridization of local search in AGI-AntMiner has improved the exploitation mechanism which leads to the discovery of more accurate classification rules. The new pre-pruning and postpruning techniques have improved the pruning ability to produce shorter classification rules which are easier to interpret by the users. Thus, the proposed AGI-AntMiner algorithm is capable in conducting an efficient search in finding the best classification rules that balance the classification accuracy and model complexity to overcome overfitting and local optima problems. 2020 Thesis https://etd.uum.edu.my/8786/ https://etd.uum.edu.my/8786/1/Deposit%20Permission_s901983.pdf text eng staffonly https://etd.uum.edu.my/8786/2/s901983_01.pdf text eng public https://etd.uum.edu.my/8786/3/s901983_references.docx text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Ku Mahamud, Ku Ruhana Allwawi, Rafid sagban Abbood
topic	QA Mathematics
spellingShingle	QA Mathematics Al-Behadili, Hayder Naser Khraibet An adaptive ant colony optimization algorithm for rule-based classification
description	Classification is an important data mining task with different applications in many fields. Various classification algorithms have been developed to produce classification models with high accuracy. Differing from other complex and difficult classification models, rules-based classification algorithms produce models which are understandable for users. Ant-Miner is a variant of ant colony optimisation and a prominent intelligent algorithm widely use in rules-based classification. However, the Ant-Miner has overfitting and easily falls into local optima problems which resulted in low classification accuracy and complex classification rules. In this study, a new Ant-Miner classifier is developed, named Adaptive Genetic Iterated-AntMiner (AGI-AntMiner) that aims to avoid local optima and overfitting problems. The components of AGI-AntMiner includes: i) an Adaptive AntMiner which is a prepruning technique to dynamically select the appropriate threshold based on the quality of the rules; ii) Genetic AntMiner that improves the post-pruning by adding/removing terms in a dual manner; and, iii) an Iterated Local Search-AntMiner that improves exploitation based on multiple-neighbourhood structure. The proposed AGI-AntMiner algorithm is evaluated on 16 benchmark datasets of medical, financial, gaming and social domains obtained from the University California Irvine repository. The algorithm’s performance was compared with other variants of Ant-Miner and state-of-the-art rules-based classification algorithms based on classification accuracy and model complexity. Experimental results proved that the proposed AGI-AntMiner algorithm is superior in two (2) aspects. Hybridization of local search in AGI-AntMiner has improved the exploitation mechanism which leads to the discovery of more accurate classification rules. The new pre-pruning and postpruning techniques have improved the pruning ability to produce shorter classification rules which are easier to interpret by the users. Thus, the proposed AGI-AntMiner algorithm is capable in conducting an efficient search in finding the best classification rules that balance the classification accuracy and model complexity to overcome overfitting and local optima problems.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Al-Behadili, Hayder Naser Khraibet
author_facet	Al-Behadili, Hayder Naser Khraibet
author_sort	Al-Behadili, Hayder Naser Khraibet
title	An adaptive ant colony optimization algorithm for rule-based classification
title_short	An adaptive ant colony optimization algorithm for rule-based classification
title_full	An adaptive ant colony optimization algorithm for rule-based classification
title_fullStr	An adaptive ant colony optimization algorithm for rule-based classification
title_full_unstemmed	An adaptive ant colony optimization algorithm for rule-based classification
title_sort	adaptive ant colony optimization algorithm for rule-based classification
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2020
url	https://etd.uum.edu.my/8786/1/Deposit%20Permission_s901983.pdf https://etd.uum.edu.my/8786/2/s901983_01.pdf https://etd.uum.edu.my/8786/3/s901983_references.docx
_version_	1747828460243386368

An adaptive ant colony optimization algorithm for rule-based classification

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