Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
DTU: A Decision Tree for Uncertain Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Decision Trees for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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This demo presents a decision tree based classification system for uncertain data. Decision tree is a commonly used data classification technique. Tree learning algorithms can generate decision tree models from a training data set. When working on uncertain data or probabilistic data, the learning and prediction algorithms need handle the uncertainty cautiously, or else the decision tree could be unreliable and prediction results may be wrong. In this demo, we will present DTU, a new decision tree based classification and prediction system for uncertain data. This tool uses new measures for constructing, pruning and optimizing decision tree. These new measures are computed considering uncertain data probability distribution functions. Based on the new measures, the optimal splitting attributes and splitting values can be identified and used in the decision tree. We will show in this demo that DTU can process various types of uncertainties and it has satisfactory classification performance even when data is highly uncertain.