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Improving naive Bayes classifier by dividing its decision regions

Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第8期12卷 647-657页

作者： Zhi-yong YAN Gong-fu XU Yun-he PAN Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

Classification can be regarded as dividing the data space into decision regions separated by decision *** this paper we analyze decision tree algorithms and the NBTree algorithm from this ***,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent ***,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 *** propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes *** four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root *** SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes ***,we conduct experiments on 30 data sets from the UC Irvine (UCI) *** results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf *** experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.

关键词： Naive Bayes classifier decision region NBTree C4.5 algorithm Support vector machine (SVM)

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