NDLI: On the Investigation of Artificial Immune Systems on Imbalanced Data Classification for Power Distribution System Fault Cause Identification

Content Provider	IEEE Xplore Digital Library
Author	Le Xu Mo-Yuen Chow Timmis, J. Taylor, L.S. Watkins, A.
Copyright Year	2006
Description	Author affiliation: Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695 USA, e-mail: xule@ieee.org (Le Xu)
Abstract	Imbalanced data are often encountered in real-world applications, they may incline the performance of classification to be biased. The immune-based algorithm artificial immune recognition system (AIRS) is applied to Duke Energy distribution systems outage data and we investigate its capability to classify imbalanced data. The performance of AIRS is compared with an artificial neural network (ANN). Two major distribution fault causes, tree and lightning strike, are used as prototypes and a tailor-made measure for imbalanced data, g-mean, is used as the major performance measure. The results indicate that AIRS is able to achieve a more balanced performance on imbalanced data than ANN.
Starting Page	522
Ending Page	527
File Size	226961
Page Count	6
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688354
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial immune systems Power distribution Fault diagnosis Artificial neural networks Power system reliability Power distribution faults Lightning Animals Power system restoration Data mining
Content Type	Text
Resource Type	Article

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