Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms

Masrom, Suraya; Rahman, Rahayu Abdul; Mohamad, Masurah; Abd Rahman, Abdullah Sani; Baharun, Norhayati

doi:10.11591/ijai.v11.i3.pp1153-1163

Open access Journal SINTA 1

Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms

IAES International Journal of Artificial Intelligence · Vol. 11 No. 3 (2022) · pp. 1153–1163

DOI: 10.11591/ijai.v11.i3.pp1153-1163
ISSN: 2252-8938, 2089-4872
Published: 2022-09-01
Publisher: Institute of Advanced Engineering and Science
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This paper presents a hybrid meta-heuristic between PSO and adaptive GA operators for the optimization of features selection in the machine learning models. The hybrid PSO-GA has been designed to employ three adaptive GA operators hence three groups of features selection will be generated. The three groups of features selection were used in random forest (RF), k-nearest neighbor (k-NN), and support vector machine (SVM). The results showed that most models that used PSO-GA hybrids have achieved better accuracy than the conventional approach (using all features from the dataset). The most accurate machine learning model was SVM, which used a PSO-GA hybrid with adaptive GA mutation.

Keywords

Collaboration network of the authors

24 authors · 67 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

Universiti Teknologi MARA
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Nasiroh Omar	Affiliation unknown	4
Shahirah Mohamed Hatim	Affiliation unknown	4
Thuraiya Mohd	Affiliation unknown	3
Teoh Sian Hoon	Affiliation unknown	3
Nadirah Othman	Affiliation unknown	3
Nor Aslily Sarkam	Affiliation unknown	3
Nor Faezah Mohamad Razi	Affiliation unknown	3
Nor Faezah Mohamad Razi	Affiliation unknown	2
Mohamed Imran Mohamed Ariff	Universiti Teknologi MARA	1
Samsiah Binti Ahmad	Universiti Teknologi MARA	1
Mohammad Abdullah	Universiti Teknologi MARA	1
Nur Anim Abdul Halim	Affiliation unknown	1
Anis Zafirah Azmi	Universiti Teknologi MARA	1
Anis Zulaikha Mohd Zukri	Affiliation unknown	1
Siti Rasidah Md Sakip	Affiliation unknown	1
Syafiqah Jamil	Affiliation unknown	1
Maheran Zakaria	Affiliation unknown	1
Rahayu Abdul Rahman	Affiliation unknown	1
Normah Omar	Affiliation unknown	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Machine learning of tax avoidance detection based on hybrid  metaheuristics algorithms</dc:title>
	<dc:creator>Masrom, Suraya</dc:creator>
	<dc:creator>Rahman, Rahayu Abdul</dc:creator>
	<dc:creator>Mohamad, Masurah</dc:creator>
	<dc:creator>Abd Rahman, Abdullah Sani</dc:creator>
	<dc:creator>Baharun, Norhayati</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">automated features selection; genetic algorithm; machine learning; particle swarm optimization; tax avoidance;</dc:subject>
	<dc:description xml:lang="en-US">This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This paper presents a hybrid meta-heuristic between PSO and adaptive GA operators for the optimization of features selection in the machine learning models. The hybrid PSO-GA has been designed to employ three adaptive GA operators hence three groups of features selection will be generated. The three groups of features selection were used in random forest (RF), k-nearest neighbor (k-NN), and support vector machine (SVM). The results showed that most models that used PSO-GA hybrids have achieved better accuracy than the conventional approach (using all features from the dataset). The most accurate machine learning model was SVM, which used a PSO-GA hybrid with adaptive GA mutation.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21739</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i3.pp1153-1163</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 3: September 2022; 1153-1163</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21739/13442</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>