Induction Decision Trees for Tentative Data
A decision tree is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a classification or decision. In this paper, the authors extend such classifiers to handle data with uncertain information. Classification is one of the most efficient and widely used data mining techniques. Decision trees handle the data whose values are certain. They 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).