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In order to solve classification error problems of support vector machine, which was used in the telecommunication business supporting system (BOSS), caused by the unbalanced ratio of positive samples, which stand for proper states of BOSS, and negative samples, which stand for the improper states, an unbalanced penalty SVM algorithm was proposed. In the proposed algorithm, values of the penalties were inverse to the ratio of the numbers of positive and negative samples, which means that the number of samples is higher, the lower the penalty coefficient. At last, in order to prove the effectiveness of the proposed algorithm, an experiment was conducted on the classification of running data of BOSS. The result of the experiment proved that the proposed SVM algorithm greatly reduces the negative sample misclassification when the ratio of positive and negative samples was not balanced, which proved the validity of the proposed algorithm.

An Unbalanced Penalty SVM for Fault Identification of BOSS