Self-advising support vector machine

The Support Vector Machine (SVM) is one of the most popular machine learning algorithms for classification and regression. SVM displays outstanding performance when utilized in many applications. However, different approaches have been proposed in order to improve its performance in general and in special cases. This paper proposes a new method of improving SVM performance in general. This method can be applied to all the types of SVMs that have differing kernel types. The key aim of the proposed approach is to transfer more information from the training phase to the testing phase. This information is obtained from the misclassified data of the training phase, which is ignored in the classic SVM. Experimental results from eleven datasets from the real online resources show that this approach can improve the classification performances of C-SVM and ν-SVM without adding any parameters to the learner algorithm. Statistical tests show significance in this improvement. The proposed method has been found to improve accuracies of C-SVM and ν-SVM in more than 67% of the experiments in which 11% of these improvements are more than 5%. It must be noted that the highest improvement found with this method was 25%.