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 Bayesian classifier for uncertain data
Proceedings of the 2010 ACM Symposium on Applied Computing
Predicting incomplete gene microarray data with the use of supervised learning algorithms
Pattern Recognition Letters
Uncertainty in decision tree classifiers
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
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This paper deals with the problem of missing values in decision trees during classification. Our approach is derived from the ordered attribute trees method, proposed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Our method takes into account the dependence between attributes by using Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we present tests performed on several databases using our approach and Quinlan's method. We also measure the quality of our classification results. Finally, we discuss some perspectives.