基于空间重叠度的DTBSVM多类分类算法

针对传统的DTBSVM算法中判断类间的可分的难易程度时可能造成的错误判断，提出了基于空间重叠度的DTSVM多类分类方法。该方法通过计算已知的类别样本在空间中的重叠度，合并有重叠的类，组合为一个新的类，再基于一种有效的类间可分性准则进行划分，使得容易划分的类能从决策树的根节点开始逐层分割出来，再划分有类间重叠的类，这样就可以尽量地避免“误差累积”的风险，构造出分类效果好的决策树结构。实验结果表明，该方法大大提升了DTSVM多类分类算法的分类正确率。

DTBSVM multi-class classification algorithm based on space overlapping region

For the traditional DTBSVM algorithm may have the misjudgement of the difficulty to separate the classes, a method which is based on the space overlapping region DTSVM multi-classification is proposed. The method will compute the space overlapping region by the labeled samples, then merges those classes who have the overlapped regions, and combines them into a new class, then uses an effective extra-class measurement of separability to devide the new labeled classes, to make sure that those classes who are easy to be partitioned will be separated from the root of the decision tree. It separates those classes who have overlapping regions, if do like this, the algorithm will avoid the risk of the cumulative of error successfully, and constructs better classification results decision tree. The experiment shows that the method will enhance the accuracy of DTBSVM multi-classification.