Mining uncertain data with probabilistic guarantees
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
A discretization algorithm for uncertain data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Feature selection with mutual information for uncertain data
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Computers and Electrical Engineering
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
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Improving classification accuracy on uncertain data by considering multiple subclasses
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Confidence estimation of feedback information for logicdiagnosis
Engineering Applications of Artificial Intelligence
Mining frequent serial episodes over uncertain sequence data
Proceedings of the 16th International Conference on Extending Database Technology
Distance-based feature selection from probabilistic data
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification algorithm for uncertain data with a pdf. Our key solution is to extend the class conditional probability estimation in the Bayes model to handle pdf’s. Extensive experiments on UCI datasets show that the accuracy of naive Bayes model can be improved by taking into account the uncertainty information.