Open AccessA framework to select a classification algorithm in electricity fraud detection
Author(s) -
Sisa Pazi,
Chantelle M. Clohessy,
Gary D. Sharp
Publication year - 2020
Publication title -
south african journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.317
H-Index - 61
eISSN - 1996-7489
pISSN - 0038-2353
DOI - 10.17159/sajs.2020/8189
Subject(s) - confusion matrix , computer science , metric (unit) , resampling , electricity , algorithm , confusion , machine learning , support vector machine , identification (biology) , classifier (uml) , naive bayes classifier , revenue , data mining , statistical classification , artificial intelligence , energy consumption , finance , engineering , operations management , psychology , botany , electrical engineering , psychoanalysis , biology , economics
In the electrical domain, a non-technical loss often refers to energy used but not paid for by a consumer. The identification and detection of this loss is important as the financial loss by the electricity supplier has a negative impact on revenue. Several statistical and machine learning classification algorithms have been developed to identify customers who use energy without paying. These algorithms are generally assessed and compared using results from a confusion matrix. We propose that the data for the performance metrics from the confusion matrix be resampled to improve the comparison methods of the algorithms. We use the results from three classification algorithms, namely a support vector machine, k-nearest neighbour and naïve Bayes procedure, to demonstrate how the methodology identifies the best classifier. The case study is of electrical consumption data for a large municipality in South Africa.