Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Dealing with Missing Values in a Probabilistic Decision Tree during Classification
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Top 10 algorithms in data mining
Knowledge and Information Systems
Possibilistic classifiers for uncertain numerical data
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
An associative classifier for uncertain datasets
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
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Data uncertainty is widespread in a variety of applications. This paper proposes a new Bayesian classification algorithm for classifying uncertain data. In the paper, we apply probability and statistics theory on uncertain data model, and provide solutions for model parameter estimation for both uncertain numerical data and uncertain categorical data. We also prove the correctness of the solutions. The experimental results demonstrate the proposed uncertain Bayesian classifier can be efficiently constructed, and it significantly outperforms the traditional Bayesian classifier in prediction accuracy when data is highly uncertain.