Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Hierarchical Density-Based Clustering of Uncertain Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Cleaning disguised missing data: a heuristic approach
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting temporal contexts in text classification
Proceedings of the 17th ACM conference on Information and knowledge management
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Uncertain data mining: an example in clustering location data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
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Uncertainty is often inherent to data and still there are just a few data mining algorithms that handle it. In this paper we focus on how to account for uncertainty in classification algorithms, in particular when data attributes should not be considered completely truthful for classifying a given sample. Our starting point is that each piece of data comes from a potentially different context and, by estimating context probabilities of an unknown sample, we may derive a weight that quantifies their influence. We propose a lazy classification strategy that incorporates the uncertainty into both the training and usage of classifiers. We also propose uK-NN, an extension of the traditional K-NN that implements our approach. Finally, we illustrate uK-NN, which is currently being evaluated experimentally, using a document classification toy example.