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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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A streaming ensemble algorithm (SEA) for large-scale classification
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Machine Learning
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ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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Pattern Recognition
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ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
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ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Statistical Analysis and Data Mining
Information Sciences: an International Journal
Pattern Recognition Letters
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A fundamental assumption often made in supervised classification is that the problem is static, i.e. the description of the classes does not change with time. However many practical classification tasks involve changing environments. Thus designing and testing classifiers for changing environments are of increasing interest and importance. A number of benchmark data sets are available for static classification tasks. For example, the UCI machine learning repository is extensively used by researchers to compare algorithms across various domains. No such benchmark datasets are available for changing environments. Also, while generating data for static environments is relatively straightforward, this is not so for changing environments. The reason is that an infinite amount of changes can be simulated, and it is difficult to define which ones will be realistic and hence useful. In this paper we propose a general framework for generating data to simulate changing environments. The paper gives illustrations of how the framework encompasses various types of changes observed in real data and also how the two most popular simulation models (STAGGER and moving hyperplane) are represented within.