An improved SVM algorithm based on normalization and Liu-Transformation

Abstract

The support vector machine (SVM) classifier is a popular and appealing classifier .It could be improved by taking some transformation about the original data before classification even sometimes its performance is not good,. In our previous paper, two transformations, NWFE-Transformation and Liu-Transformation are considered. The results showed that the SVM with our Liu-Transformation algorithm has the best performance. In this paper, we considered the further improved SVM algorithm based on not only the Liu-transformation but also the well known normalization, For evaluating the performances of the SVM without any transformation and normalization, the SVM with NWFE-Transformation and Liu-Transformation, respectively, the SVM with one of above two transformations and the well known normalization, a real data experiment by using 5-fold and Leave-one-out Cross-Validation accuracy is conducted. Experimental result shows that the SVM with the proposed Liu-Transformation algorithm and the well known normalization algorithm has the best performance.