C4.5: programs for machine learning
C4.5: programs for machine learning
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Learning to Use a Learned Model: A Two-Stage Approach to Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Finding frequent items in probabilistic data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
A Bayesian classifier for uncertain data
Proceedings of the 2010 ACM Symposium on Applied Computing
Direct mining of discriminative patterns for classifying uncertain data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Associative classifier for uncertain data
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Outlier detection on uncertain data: Objects, instances, and inferences
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
On pruning for top-k ranking in uncertain databases
Proceedings of the VLDB Endowment
UNN: a neural network for uncertain data classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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The classification of uncertain datasets is an emerging research problem that has recently attracted significant attention. Some attempts to devise a classification model with uncertain training data have been proposed using decision trees, neural networks, or other approaches. Among those, the associative classifiers have inspired some of the uncertain classification algorithms given their promising results on standard datasets. We propose a novel associative classifier for uncertain data. Our method, Uncertain Associative Classifier (UAC) is efficient and has an effective rule pruning strategy. Our experimental results on real datasets show that in most cases, UAC reaches better accuracies than the state of the art algorithms.