Improving support vector machine by preprocessing data with decision tree

Support vector machine(SVM) has been widely used for its outstanding performance, but, it still has flaws. One of them is that SVM is unit sensitive. In this paper, we analyze how will the different units effect the SVM. Then, we propose a preprocess method not only to conquer this flaw, but also improve the generalization precision of SVM. The preprocess method is base on decision tree(DT). The idea is using DT to train the data first, then, scaling the data base on the outcome decision tree. Finally, SVM is adapted on the new data for training and prediction. Experimental results on real data show remarkable improvement of generalization precision.