A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Clustering of Uncertain Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Clustering Uncertain Data Using Voronoi Diagrams
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Decision Trees for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Constraint projections for ensemble learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Naive Bayes Classification of Uncertain Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Decision Trees for Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
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
Improving classification accuracy on uncertain data by considering multiple subclasses
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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We study the problem of classification on uncertain objects whose locations are uncertain and described by probability density functions (pdf). We propose a novel supervised UK-means algorithm for classifying uncertain objects to overcome the computation bottleneck of existing algorithms. Additionally, we consider to select features that can capture the relevant properties of uncertain data. We experimentally demonstrate that our proposed approaches are more efficient than existing algorithms and can attain comparatively accurate results on non-overlapping data sets.