Mining uncertain data for constrained frequent sets
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Frequent subgraph pattern mining on uncertain graph data
Proceedings of the 18th ACM conference on Information and knowledge management
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
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
Accelerating probabilistic frequent itemset mining: a model-based approach
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Associative classifier for uncertain data
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Visual recognition with humans in the loop
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Uncertainty in decision tree classifiers
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Analyzing compliance of service-based business processes for root-cause analysis and prediction
ICWE'10 Proceedings of the 10th international conference on Current trends in web engineering
Classify uncertain data with decision tree
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Classifier ensemble for uncertain data stream classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining uncertain data streams using clustering feature decision trees
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Distance-based feature selection on classification of uncertain objects
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
Information Sciences: an International Journal
Nearest Neighbor-Based Classification of Uncertain Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Improving classification accuracy on uncertain data by considering multiple subclasses
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Service Oriented Computing and Applications
EMU: An expectation maximization based approach for clustering uncertain data
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from measurement/quantisation errors, data staleness, multiple repeated measurements, etc. The value uncertainty is represented by multiple values forming a probability distribution function (pdf). We discover that the accuracy of a decision tree classifier can be much improved if the whole pdf, rather than a simple statistic, is taken into account. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Since processing pdf's is computationally more costly, we propose a series of pruning techniques that can greatly improve the efficiency of the construction of decision trees.