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Data Mining on the Sisyphus Dataset: Evaluation and Integration of Results

Thomas Gartner, Shaomin Wu, Peter A. Flach, Data Mining on the Sisyphus Dataset: Evaluation and Integration of Results. Integrating Aspects of Data Mining, Decision Support and Meta-Learning. Christophe Giraud-Carrier, Nada Lavrac, Steve Moyle, (eds.), pp. 69–80. September 2001. No electronic version available. External information

Abstract

This paper describes our work on the Sisyphus challenge dataset, which includes both classification and clustering tasks. We present our work in the context of the CRISP-DM methodology. Further key aspects of the work are the evaluation and integration of multiple models by means of ROC analysis. We indicate a simple method of forcing classifiers to cover the whole of the ROC space. In conclusion, we outline several promising research directions.

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