Developing Decision Trees for Handling Uncertain Data
Classification is one of the most efficient and widely used data mining techniques. In classification, Decision trees can handle high dimensional data, and their representation is intuitive and generally easy to assimilate by humans. Decision trees handle the data whose values are certain. The authors extend such classifiers i.e., decision trees to handle uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include data staleness, and 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 (pdf's).