Abstract
This paper proposes a new risk assessment method based on the attribute reduction theory of rough set and multiclass SVM classification. Rough set theory is introduced for data attribute reduction and multiclass SVM is used for automatic assessment of risk levels. Redundant features of data are deleted that can reduce the computation complexity of multiclass SVM and improve the learning and the generalization ability. Multiclass SVM trained with the empirical data can predict the risk level. Experiment shows that the predict result has relatively high precision, and the method is validity for power network risk assessment.
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