Using Emerging Patterns to Construct Weighted Decision Trees
IEEE Transactions on Knowledge and Data Engineering
World Wide Web
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Data preparation techniques for improving rare class prediction
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Optimizing airline passenger prescreening systems with Bayesian decision models
Computers and Operations Research
An algorithm for extracting rare concepts with concise intents
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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The problem of classifying rarely occurring cases is faced in many real life applications. The scarcity of the rare cases makes it difficult to classify them correctly using traditional classifiers. In this paper, we propose a new approach to use emerging patterns (EPs) and decision trees (DTs) in rare-class classification (EPDT). EPs are those itemsets whose supports in one class are significantly higher than their supports in the other classes. EPDT employs the power of EPs to improve the quality of rare-case classification. To achieve this aim, we first introduce the idea of generating new non-existing rare-class instances, and then we over-sample the most important rare-class instances. Our experiments show that EPDT outperforms many classification methods.