SVM prediction of the attestation success on the base of the poll results

Abstract

The problem of the data analysis in the educational sphere in the context of prediction of the passing's success of the final state attestation by the graduates of the secondary school has been considered. Such data can be imbalanced substantially. To solve this problem it is offered to use the SVM classifiers on the base of the modified PSO algorithm, which allows choosing the kernel function type, the values of the kernel function parameters and the value of the regularization parameter simultaneously. In advance, the different rebalancing strategies, based on the basic SMOTE algorithm, can be applied for rebalance the classes in the experimental datasets. The prediction results with the use of the SVM classifiers on the base of the modified PSO algorithm and the different rebalancing strategies have been presented and compared with the prediction results received on the base of the most known software packages, such as Statistica StatSoft and IBM SPSS Modeler.