Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms
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)...
| Main Authors: | Masrom, S., Rahman, R.A., Mohamad, M., Rahman, A.S.A., Baharun, N. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.33319 / |
| Published: |
Institute of Advanced Engineering and Science
2022
|
| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133466175&doi=10.11591%2fijai.v11.i3.pp1153-1163&partnerID=40&md5=8b1ea8bb2e809d576b38feec87a8337b http://eprints.utp.edu.my/33319/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
utp-eprints.33319 |
|---|---|
| recordtype |
eprints |
| spelling |
utp-eprints.333192022-07-26T06:40:19Z Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms Masrom, S. Rahman, R.A. Mohamad, M. Rahman, A.S.A. Baharun, N. 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. © 2022, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133466175&doi=10.11591%2fijai.v11.i3.pp1153-1163&partnerID=40&md5=8b1ea8bb2e809d576b38feec87a8337b Masrom, S. and Rahman, R.A. and Mohamad, M. and Rahman, A.S.A. and Baharun, N. (2022) Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms. IAES International Journal of Artificial Intelligence, 11 (3). pp. 1153-1163. http://eprints.utp.edu.my/33319/ |
| institution |
Universiti Teknologi Petronas |
| collection |
UTP Institutional Repository |
| description |
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. © 2022, Institute of Advanced Engineering and Science. All rights reserved. |
| format |
Article |
| author |
Masrom, S. Rahman, R.A. Mohamad, M. Rahman, A.S.A. Baharun, N. |
| spellingShingle |
Masrom, S. Rahman, R.A. Mohamad, M. Rahman, A.S.A. Baharun, N. Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms |
| author_sort |
Masrom, S. |
| title |
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms |
| title_short |
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms |
| title_full |
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms |
| title_fullStr |
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms |
| title_full_unstemmed |
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms |
| title_sort |
machine learning of tax avoidance detection based on hybrid metaheuristics algorithms |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2022 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133466175&doi=10.11591%2fijai.v11.i3.pp1153-1163&partnerID=40&md5=8b1ea8bb2e809d576b38feec87a8337b http://eprints.utp.edu.my/33319/ |
| _version_ |
1741197835819286528 |
| score |
11.62408 |