A Novel Meta-heuristic Framework for Solving Power Theft Detection Problem: Cheetah Optimization Algorithm

Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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JR_IECO-5-1_007

تاریخ نمایه سازی: 20 تیر 1401

Abstract:

In this paper, a two-level stacking technique with feature selection is used to detect power theft. The first level of this technique uses base classifiers such as support vector machine (SVM), naïve Bayes (NB), and AdaBoost selected by evaluating the F-score and diversity criteria. The appropriate features of the base classifiers are selected using a new feature selection algorithm based on the cheetah optimization algorithm (CHOA). This algorithm ensures diversification and intensification in each step of running by adjusting the Attention parameter of the cheetahs. In the second level, a single-layer perceptron (SLP) model is used to obtain the weight of the base classifiers and combine their predictions. The proposed framework is evaluated on the Irish Social Science Data Archive (ISSDA) dataset, and MATLAB R۲۰۲۰b is used for simulation and evaluation. The results of the accuracy, recall, precision, and F-score, specificity, and receiver operating characteristic (ROC) criteria indicated the high efficiency of the proposed framework in detecting power theft.

Authors

Hassan Ghaedi

Department of Computer, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

Seyed Reza Kamel Tabbakh Farizani

Department of Computer, Mashhad Branch, Islamic Azad University, Mashhad, Iran

Reza Gaemi

Islamic Azad University, Quchan Branch

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