On the investigation of artificial immune systems on imbalanced data classification for power distribution system fault cause identification

Le Xu*, Mo Yuen Chow, Jon Timmis, Leroy S. Taylor, Andrew Watkins

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

1 Dyfyniadau(SciVal)

Crynodeb

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.

Iaith wreiddiolSaesneg
Teitl2006 IEEE Congress on Evolutionary Computation, CEC 2006
Tudalennau522-527
Nifer y tudalennau6
StatwsCyhoeddwyd - 2006
Digwyddiad2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Hyd: 16 Gorff 200621 Gorff 2006

Cyfres gyhoeddiadau

Enw2006 IEEE Congress on Evolutionary Computation, CEC 2006

Cynhadledd

Cynhadledd2006 IEEE Congress on Evolutionary Computation, CEC 2006
Gwlad/TiriogaethCanada
DinasVancouver, BC
Cyfnod16 Gorff 200621 Gorff 2006

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