Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
A Bayesian approach to use emerging patterns for classification
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Using Emerging Patterns and Decision Trees in Rare-Class Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
EP-based robust weighting scheme for fuzzy SVMs
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Mining Layered Grammar Rules for Action Recognition
International Journal of Computer Vision
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Decision trees (DTs) represent one of the most important and popular solutions to the problem of classification. They have been shown to have excellent performance in the field of data mining and machine learning. However, the problem of DTs is that they are built using data instances assigned to crisp classes. In this paper, we generalize decision trees so that they can take into account weighted classes assigned to the training data instances. Moreover, we propose a novel method for discovering weights for the training instances. Our method is based on emerging patterns (EPs). EPs are those itemsets whose supports (probabilities) in one class are significantly higher than their supports (probabilities) in the other classes. Our experimental evaluation shows that the new proposed method has good performance and excellent noise tolerance.