A New Support Vector Machine Method for Unbalanced Data Treatment

In SVM algorithm,when training sets with uneven class sizes are used,the prediction result of classifier is undesirably biased towards the class with more samples in the training set.That is to say,the larger the sample size,the smaller the classification error,whereas the smaller the sample size,the larger the classification error.Aiming at this orientation problem and with the analysis of the cause of it,an improved method based on genetic crossover operator was proposed,for the training set with small size generate new samples by using crossover operation,thereby compensates for the unfavorable impact caused by the bias of the training data class size.Simulation experiment results on UCI stander data shows that the proposed method has better classification accurate compared with stander support vector machine method.