A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems.

[1]  Carlos León,et al.  MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques , 2006, ICCSA.

[2]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[3]  A.H. Nizar,et al.  Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method , 2008, IEEE Transactions on Power Systems.

[4]  A.H. Nizar,et al.  A Data Mining Based NTL Analysis Method , 2007, 2007 IEEE Power Engineering Society General Meeting.

[5]  João Paulo Papa,et al.  Supervised pattern classification based on optimum‐path forest , 2009, Int. J. Imaging Syst. Technol..

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  Zong Woo Geem,et al.  Novel derivative of harmony search algorithm for discrete design variables , 2008, Appl. Math. Comput..

[8]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004 .

[9]  Alexandre X. Falcão,et al.  Motion segmentation and activity representation in crowds , 2009 .

[10]  João Paulo Papa,et al.  What is the importance of selecting features for non-technical losses identification? , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[11]  Sieh Kiong Tiong,et al.  Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines , 2010, IEEE Transactions on Power Delivery.

[12]  Erik D. Goodman,et al.  Swarmed feature selection , 2004, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04).

[13]  Syed Khaleel Ahmed,et al.  Detection of abnormalities and electricity theft using genetic Support Vector Machines , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[14]  C C O Ramos,et al.  A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest , 2011, IEEE Transactions on Power Systems.

[15]  Qiang Shen,et al.  Two new approaches to feature selection with harmony search , 2010, International Conference on Fuzzy Systems.

[16]  S.K. Tiong,et al.  Non-Technical Loss analysis for detection of electricity theft using support vector machines , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[17]  João Paulo Papa,et al.  A New Variant of the Optimum-Path Forest Classifier , 2008, ISVC.

[18]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[19]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..