期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2013
卷号：3
期号：1
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The decision tree (DT) approach is most useful in classification problem. In conventional decision tree learning the weights of every training instances are set to one or equal value, which contradicts general intuition. In this paper, we proposed a new decision tree learning algorithm by assigning appropriate weights to each training instance in the training data that increases classification accuracy of the decision tree model. The main advantage of this proposed approach is to set appropriate weights to training instances using naïve Bayesian classifier before trying to construct the decision tree. In our approach the training instances are assigned to weight values based on the posterior probability. The training instances having less weight values are either noisy or posses unique characteristics compared to other training instances. The experimental results manifest that the proposed approach for decision tree construction can achieve high classification accuracy with compare to traditional decision tree algorithms on different types of benchmark datasets from UCI machine learning repository.
关键词：Bayesian Classifier; Classification; Decision Tree; Training Instance; Weights
