eprintid: 16429
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/64/29
datestamp: 2023-12-19 03:22:57
lastmod: 2023-12-19 03:22:57
status_changed: 2023-12-19 03:06:14
type: article
metadata_visibility: show
creators_name: Masrom, S.
creators_name: Rahman, R.A.
creators_name: Mohamad, M.
creators_name: Rahman, A.S.A.
creators_name: Baharun, N.
title: Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms
ispublished: pub
note: cited By 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. © 2022, Institute of Advanced Engineering and Science. All rights reserved.
date: 2022
publisher: Institute of Advanced Engineering and Science
official_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
id_number: 10.11591/ijai.v11.i3.pp1153-1163
full_text_status: none
publication: IAES International Journal of Artificial Intelligence
volume: 11
number: 3
pagerange: 1153-1163
refereed: TRUE
issn: 20894872
citation:   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.  ISSN 20894872