期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：3
页码：3069-3073
出版社：TechScience Publications
摘要：Classification is one of the important data mining techniques and Decision Tree is a most common structure for classification which is used in many applications. Decision tree classifier works on precise and known data. Traditional classifier extended to handle uncertain data caused by faulty data collection processes. To handle uncertainty feature value is represented by probability distribution function instead of single value. This improves accuracy of decision tree as complete information is used. Probability density function (PDF) requires many calculations. Pruning techniques are used to remove unwanted intervals and to reduce execution time. In this paper bagging method is combined with decision tree technique to stabilize the performance of decision tree and to improve accuracy of decision tree
