一种基于支持向量机集成的决策树分类算法  

A Support Vector Machine Ensemble-based Decision Tree Classification Algorithm

作　　者：姜锐[1] 衣马木艾山.阿布都力克木 

机构地区：[1]乌鲁木齐职业大学信息工程学院,乌鲁木齐830002

出　　处：《科技通报》2013年第8期145-147,共3页Bulletin of Science and Technology

基　　金：国家自然科学基金项目(60662002)

摘　　要：传统决策树算法有好的可理解性,而支持向量机集成方法有好的分类性能。本文基于二者的优点提出了一种基于支持向量机集成的决策树分类算法,首先在样本集合上训练支持向量机集成分类器。然后随机生成一定数目的特征向量并加以标注,构造辅助样本。最后在辅助样本的帮助下生成新的决策树。由于最终结果由决策树给出,所以本文算法具有很好的可理解性。此外在辅助样本的帮助下,算法的分类性能也得到提高。在UCI标准数据集与新闻文本数据集上的实验充分验证了本文算法的合理性。Traditional decision tree algorithm has good comprehensibility, and support vector machine integration ap- proach has good classification accuracy. Based on their advantages, we propose a support vector machine ensemble- based decision tree algorithm. Firstly, train a support vector machine ensemble classifier on training set. Then compute the labels of feature vectors to construct auxiliary samples. Finally, we train a decision tree under the help of auxiliary samples. Since the results are given by decision tree, the algorithm has good comprehensibility. Moreover, the perform is also improved with the help of the auxiliary samples. Experiments on UCI standard data sets and news text data set fully explain the reasonableness of the proposed algorithm.

关 键 词：决策树 支持向量机集成 特征向量 可理解性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]