Optimal hyperplane tendency and a large number of positive sample misclassifications often appear when the standard support vector machine (SVM) is employed to classify unbalanced data.So several causes and corresponding countermeasures for the perspective of SVM misclassifying unbalanced data are discussed.Considering the characteristics of SVM that optimal hyperplane is only decided by a small amount of support vectors

a novel SVM mathematical model based on negative boundary sample cutting strategy is constructed.However

this model has better recognition performance on positive samples only when the training-cutting step of negative samples is carried out many times

which is a time-consuming process.To replace it with the equivalent cutting hyperplane technique which can cut more negative samples at one time

an unbalanced SVM algorithm coupling negative-samples cutting with asymmetric misclassification cost is proposed.To further enhance the classification ability of this algorithm on unbalanced data

an improved sine cosine algorithm (ISCA) is presented to optimize the biased constant of the cutting hyperplane.Experimental results verify the optimized necessity of the biased constant of the cutting hyperplane

the advanced optimization performance of ISCA algorithm and the outstanding recognition performance of the proposed algorithm on unbalanced datasets

respectively.