Title :
Safety Assessment in Power Supply Enterprise Based on Rough Set and Support Vector Machine
Author :
Sun, Wei ; Zhang, Xing
Author_Institution :
North China Electr. Power Univ., Baoding
Abstract :
Considering safety assessment indexes of power supply enterprise are considerable, an hybrid model based on rough sets (RS) and support vector machine(SVM) is proposed: rough sets, as a anterior preprocessor of SVM, can find out the kernel factors influencing the safety of power supply enterprise by means of attribute reduction algorithm, and then, using them as the input vectors of SVM, the safety assessment is conducted. Experiment results compared with traditional SVM model show that the training rapidity and accuracy of the RS-SVM model are both evidently improved.
Keywords :
power engineering computing; power markets; rough set theory; support vector machines; attribute reduction algorithm; power supply enterprise; rough set; safety assessment; support vector machine; Data preprocessing; Electrical safety; Energy management; Power supplies; Power system management; Power system modeling; Rough sets; Sun; Support vector machine classification; Support vector machines;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.647