期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2014
卷号：5
期号：4
页码：89-93
语种：English
出版社：Ayushmaan Technologies
摘要：Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process like multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives (such as mean and median), we discover that the accuracy of a decision tree classifier can be much improved if the “complete information” of a data item (taking into account the probability density function (PDF)) is utilized.Extensive experiment results shown that the resulting classifiers are more accurate than those using value averages. Since processing PDF’s is computationally more costly than processing single values (e.g., averages), decision tree construction on uncertain data is more CPU demanding than that for certain data, To tackle this pruning techniques are used. We extend classical decision tree building algorithms to handle data tuples with uncertain values using Uncertain Decision Tree -Cumulative Distributive Function (UDT-CDF).
