Benchmarking Attribute Selection Techniques for Discrete Class Data Mining

@article{Hall2003BenchmarkingAS,
  title={Benchmarking Attribute Selection Techniques for Discrete Class Data Mining},
  author={Mark A. Hall and Geoff Holmes},
  journal={IEEE Trans. Knowl. Data Eng.},
  year={2003},
  volume={15},
  pages={1437-1447}
}
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 identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant, and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. Attribute selection generally involves a… CONTINUE READING
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