Resolution Trees for Tentative Information
Traditional decision tree classifiers work with data whose values are known and precise. The authors extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments have been conducted which show that the resulting classifiers are more accurate than those using value averages. Since processing pdfs 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. They extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process.