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Chess (Domain Theories) Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info.

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Mark A. Hall. Department of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Machine Learning. Doctor of Philosophy at The University of Waikato. 1999.

2 In comparison, CFS-P using the MDL measure considered on average less than 50 derived attributes on the chess end-game dataset. 141 IB1 Domain CFS-UC CFS-P CFS-RELIEF mu 98.48 0.1 98.64 0.3+ 99.72 0.2+ vo 95.60 1.0 95.60 1.0 95.12 1.2 - v1 88.35 2.1 88.35 2.1 88.17 1.9 cr 85.61 1.0 85.61 1.0 85.61


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