Diversity Control in the Hybridization GA-PSO with Fuzzy Adaptive Inertial Weight

Noronha, Rodrigo Possidônio

doi:10.1109/iciccs51141.2021.9432269

Cited by 1 publication

(1 citation statement)

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“…It is anticipated in this research that the PSO-GA hybridization [11], [12] can be very useful to be deployed for the application of machine learning, mainly for the problem that involved real cases dataset. The results of machine learning models from our previous studies in [13], [14] on the tax avoidance detection were undesirable due to the problem of very weak correlation among the dataset features.…”

Section: Introductionmentioning

confidence: 99%

Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms

Masrom

Rahman²,

Mohamad³

et al. 2022

IJ-AI

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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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