The nature of statistical learning theory
The nature of statistical learning theory
Principles of data mining
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Learning from positive and unlabeled examples with different data distributions
ECML'05 Proceedings of the 16th European conference on Machine Learning
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In many real-world data mining tasks, the coverage of the target concept may change as the time changes. For example, the coverage of "learned knowledge" of a student today may be different from his/er "learned knowledge" tomorrow, since the "learned knowledge" of the student is in expanding everyday. In order to learn a model capable of making accurate predictions, the evolution of the concept must be considered, and thus, a series of data sets collected at different time is needed. However, in many cases there is only a single data set instead of a series of data sets. In other words, only a single snapshot of the data along the time axis is available. In this paper, we show that for positive class expansion, i.e., the coverage of the target concept is in expanding as illustrated in the above "learned knowledge" example, we can learn an accurate model from the single snapshot data with the help of domain knowledge given by user. The effectiveness of the proposed framework is validated in experiments.