Variance considered machines: Modification of optimal hyperplanes in support vector machines

We propose a new classification algorithm, variance considered machine (VCM), by modifying optimal hyperplanes of the support vector machine (SVM). The SVM is a good method to calculate a slope of optimal hyperplanes with maximal margin. However, this algorithm neglects to consider variances and prior probabilities of the data. It can increase probabilities of error. To solve this problem, the VCM shifts the optimal hyperplanes of the SVM according to variances and prior probabilities. Therefore, the VCM has not only maximal margin, which is an advantage of the SVM, but also lower error probability. Through 10 case examples with different variances and prior probabilities, we demonstrated the superiority of the VCM by comparing the results of the SVM and VCM.