Abstract
Non-technical loss detection represents a very high cost to power supply companies. Finding classifiers that can deal with this problem is not easy as they have to face a high imbalance scenario with noisy data. In this paper we propose to use Optimal F-measure Classifier (OFC) and Linear F-measure Classifier (LFC), two novel algorithms that are designed to work in problems with unbalanced classes. We compare both algorithm performances with other previously used methods to solve automatic fraud detection problem.
Chapter PDF
Similar content being viewed by others
References
Alcetegaray, D., Kosut, J.: One class SVM para la detección de fraudes en el uso de energía eléctrica. Trabajo Final Curso de Reconocimiento de Patrones, Dictado por el IIE-Facultad de Ingeniera-UdelaR (2008)
dos Angelos, E., Saavedra, O., Corts, O., De Souza, A.: Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Transactions on Power Delivery 26(4), 2436–2442 (2011)
Barandela, R., Garcia, V.: Strategies for learning in class imbalance problems. Pattern Recognition 849–851 (2003)
Batista, G., Pratti, R., Monard, M.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6, 20–29 (2004)
Biscarri, F., Monedero, I., Leon, C., Guerrero, J.I., Biscarri, J., Millan, R.: A data mining method based on the variability of the customer consumption - A special application on electric utility companies, vol. AIDSS, pp. 370–374. Inst. for Syst. and Technol. of Inf. Control and Commun. (2008)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, USA (1996)
Chawla, Nitesh V., Lazarevic, Aleksandar, Hall, Lawrence O., Bowyer, Kevin W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavrač, Nada, Gamberger, Dragan, Todorovski, Ljupčo, Blockeel, Hendrik (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107–119. Springer, Heidelberg (2003)
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Depuru, S.S.S.R., Wang, L., Devabhaktuni, V.: Support vector machine based data classification for detection of electricity theft. In: 2011 IEEE/PES Power Systems Conference and Exposition pp. 1–8 (2011)
Di Martino, J., Decia, F., Molinelli, J., Fernández, A.: Improving electric fraud detection using class imbalance strategies. In: International Conference In Pattern Recognition Aplications and Methods pp. 135–141 (2012)
Di Martino, M., Fernández, A., Iturralde, P., Lecumberry, F.: Novel classifier scheme for imbalanced problems. Pattern Recognition Letters 34(10), 1146–1151 (2013)
Di Martino, M., Hernández, G., Fiori, M., Fernández, A.: A new framework for optimal classifier design. Pattern Recognition 46(8), 2249–2255 (2013)
Di Martino, Matías, Decia, Federico, Molinelli, Juan, Fernández, Alicia: A novel framework for nontechnical losses detection in electricity companies. In: Latorre Carmona, Pedro, Sánchez, JSalvador, Fred, Ana L.N. (eds.) Pattern Recognition - Applications and Methods. AISC, vol. 204, pp. 109–120. Springer, Heidelberg (2013)
Filho, J., Gontijo, E., Delaiba, A., Mazina, E., Cabral, J.E., Pinto, J.O.: Fraud identification in electricity company costumers using decision tree pp. 3730–3734 (2004)
Galván, J., Elices, E., Noz, A.M., Czernichow, T., Sanz-Bobi, M.: System for detection of abnormalities and fraud in customer consumption. In: Proc. 12th IEEE/PES conf. Electric Power Supply Industry (1998)
Garca, V., Mollineda, J.S.S.R.A., Sotoca, R.A.J.M.: The class imbalance problem in pattern classification and learning, pp. 283–291 (2007)
García, V., Sánchez, J., Mollineda, R.: On the suitability of numerical performance measures for class imbalance problems. In: International Conference In Pattern Recognition Aplications and Methods, pp. 310–313 (2012)
Guo, X., Yin, Y., Dong, C., Yang, G., Zhou, G.: On the class imbalance problem. In: International Conference on Natural Computation, pp. 192–201 (2008)
Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. National Taiwan University, Taipei (2010)
Jiang, R.J.R., Tagaris, H., Lachsz, A., Jeffrey, M.: Wavelet based feature extraction and multiple classifiers for electricity fraud detection. IEEE/PES Transmission and Distribution Conference and Exhibition vol. 3 (2002)
Kolez, A., Chowdhury, A., Alspector, J.: Data duplication: an imbalance problem?. Workshop on Learning with Imbalanced Data Sets, ICML (2003)
Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)
Leon, C., Biscarri, F.X.E.L., Monedero, I.X.F.I., Guerrero, J.I., Biscarri, J.X.F.S., Millan, R.X.E.O.: Variability and trend-based generalized rule induction model to ntl detection in power companies. IEEE Transactions on Power Systems 26(4), 1798–1807 (2011)
López, V., Fernández, A., del Jesus, M.J., Herrera, F.: Cost sensitive and preprocessing for classification with imbalanced data-sets: Similar behaviour and potential hybridizations. In: International Conference In Pattern Recognition Aplications and Methods, pp. 98–107 (2012)
Markoc, Z., Hlupic, N., Basch, D.: Detection of suspicious patterns of energy consumption using neural network trained by generated samples. In: Proceedings of the ITI 2011 33rd International Conference on Information Technology Interfaces, pp. 551–556 (2011)
Monedero, Iñigo, Biscarri, Félix, León, Carlos, Guerrero, Juan I., Biscarri, Jesús, Millán, Rocío: Using regression analysis to identify patterns of non-technical losses on power utilities. In: Setchi, Rossitza, Jordanov, Ivan, Howlett, Robert J., Jain, Lakhmi C. (eds.) KES 2010, Part I. LNCS, vol. 6276, pp. 410–419. Springer, Heidelberg (2010)
Müller-Merbach, H.: Heuristics: Intelligent search strategies for computer problem solving. European Journal of Operational Research 21(2), 278–279 (1985)
Muniz, C., Vellasco, M., Tanscheit, R., Figueiredo, K.: A neuro-fuzzy system for fraud detection in electricity distribution. In: IFSA-EUSFLAT, pp. 1096–1101 (2009)
Nagi, J., Mohamad, M.: Nontechnical loss detection for metered customers in power utility using support vector machines. In: IEEE Transactions on Power Delivery, pp. 1162–1171 (2010)
Osher, S., Sethian, J.A.: Fronts propagating with curvature- dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)
Quinlan, J.: C4.5: Programs for Machine Learning. C4.5 - programs for machine learning. J. Ross Quinlan. Morgan Kaufmann Publishers (1993). http://books.google.com.uy/books?id=HExncpjbYroC
Ramos, C.C.O., Sousa, A.N.D., Papa, J.P., Falcao, A.X.: A new approach for nontechnical losses detection based on optimum-path forest. IEEE Transactions on Power Systems 26(1), 181–189 (2011)
van Rijsbergen, C.J.: Information Retrieval. Butterworth (1979)
Rodríguez, F., Lecumberry, F., Fernández, A.: Non technical loses detection - experts labels vs. inspection labels in the learning stage. In: ICPRAM 2014 - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, ESEO, Angers, Loire Valley, France, 6–8 March, 2014, pp. 624–628 (2014). http://dx.doi.org/10.5220/0004823506240628
Sforna, M.: Data mining in power company customer database. Electrical Power System Research 55, 201–209 (2000)
Tacón, Juan, Melgarejo, Damián, Rodríguez, Fernanda, Lecumberry, Federico, Fernández, Alicia: Semisupervised approach to non technical losses detection. In: Bayro-Corrochano, Eduardo, Hancock, Edwin (eds.) CIARP 2014. LNCS, vol. 8827, pp. 698–705. Springer, Heidelberg (2014)
Yap, K.S., Hussien, Z., Mohamad, A.: Abnormalities and fraud electric meter detection using hybrid support vector machine and genetic algorithm. In: 3rd IASTED Int. Conf. Advances in Computer Science and Technology, Phuket, Thailand, vol. 4 (2007)
Yap, K.S., Tiong, S.K., Nagi, J., Koh, J.S.P., Nagi, F.: Comparison of supervised learning techniques for non-technical loss detection in power utility. International Review on Computers and Software (I.RE.CO.S.) 7(2), 1828–6003 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rodriguez, F., Di Martino, M., Kosut, J.P., Santomauro, F., Lecumberry, F., Fernández, A. (2015). Optimal and Linear F-Measure Classifiers Applied to Non-technical Losses Detection. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)