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Abstract. Data engineering is generally considered to be a central issue in the development of data mining applications. The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identication of a small set of highly predictive attributes.

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Benchmarking attribute selection techniques for discrete class data mining (2003)

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Benchmarking Attribute Selection Techniques for …

Benchmarking Attribute Selection Techniques for Data Mining Mark A. Hall Geo rey Holmes Department of Computer Science, University of Waikato Hamilton, New Zealand

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 15, NO. 3, MAY/JUNE 2003 1 Benchmarking Attribute Selection Techniques for Discrete Class Data …

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Benchmarking Attribute Selection Techniques for Discrete Class Data Mining Mark A. Hall and Geoffrey Holmes Abstract—Data engineering is generally considered to be a central issue in the development of data mining applications.

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Holmes, G., Pfahringer, B., Kirkby, R., Frank, E. & Hall, M. (2002). Benchmarking attribute selection techniques for discrete class data mining.

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Holmes, G. & Hall, M.A. (2000). Benchmarking attribute selection techniques for data mining. (Working paper 00/10). Hamilton, New Zealand: University of Waikato, Department of Computer Science.