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Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms

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

DOI
10.11591/ijai.v11.i3.pp1153-1163
ISSN
2252-8938, 2089-4872
Terbit
2022-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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  • Universiti Teknologi MARA
  • Universiti Teknologi Petronas
  • Universitas Pembangunan Jaya
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Rekan penulisOrganisasiArtikel bersama
Nor Faezah Mohamad RaziUniversiti Teknologi MARA5
Shahirah Mohamed HatimUniversiti Teknologi MARA4
Nasiroh OmarUniversiti Teknologi MARA4
Thuraiya MohdUniversiti Teknologi MARA3
Teoh Sian HoonUniversiti Teknologi MARA3
Nadirah OthmanUniversiti Teknologi MARA3
Nor Aslily SarkamUniversiti Teknologi MARA3
Normah Binti OmarUniversiti Teknologi MARA2
Maheran ZakariaUniversiti Teknologi MARA2
Mohamed Imran Mohamed AriffUniversiti Teknologi MARA1
Samsiah Binti AhmadUniversiti Teknologi MARA1
Mohammad AbdullahUniversiti Teknologi MARA1
Nur Anim Abdul HalimAfiliasi tidak diketahui1
Anis Zafirah AzmiUniversiti Teknologi MARA1
Anis Zulaikha Mohd ZukriUniversiti Teknologi MARA1
Siti Rasidah Md SakipUniversiti Teknologi MARA1
Syafiqah JamilUniversiti Teknologi MARA1
Endang PitalokaAfiliasi tidak diketahui1
Yohanes Totok SuyotoUniversitas Pembangunan Jaya1
Noor Saatila Mohd IsaUniversiti Teknologi MARA1
Norhayati ZamriUniversiti Teknologi MARA1
Mohd Hadli Shah Mohamad YunusAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<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>