Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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Machine Learning
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using regression analysis to identify patterns of non-technical losses on power utilities
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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This paper presents an intelligent system to reduce Non Technical Loss (NTL) using hybrid Support Vector Machine (SVM) and Genetic Algorithm (GA). The main motivation for this research is to assist Sabah Electricity Sdn. Bhd. (SESB) to reduce their distribution loss, estimated around 15% at present in Sabah State, Malaysia. The hybrid algorithm is able to preselect customers to be inspected on-site for abnormalities or potential fraud according to their consumption patterns. SVM is a classification technique developed by Vapnik [1] but a practical difficulty of using SVM is the selection of parameters such as C and kernel parameter, σ in Gaussian RBF kernel. The purpose of choosing parameters is to get the best generalization performance. Genetic Algorithm (GA) is used to search for the best parameter of SVM classification by using combination of random and pre-populated genomes from Pre-Populated Database (PPD). It provides an increased convergence and globally optimized solutions. The algorithm has been tested using actual customer consumption data from SESB. 10 fold cross validation method is used to confirm the consistency of the detection accuracy. The paper also highlights comparison results between typical SVM and SVM-GA. The highest fraud detection accuracy for SVMGA is 94%.