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      • 2000 Working Papers
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      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2000 Working Papers
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      Benchmarking attribute selection techniques for data mining

      Hall, Mark A.; Holmes, Geoffrey
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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.
      Permanent Research Commons link: https://hdl.handle.net/10289/1026
      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 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 combination of search and attribute utility estimation plus evaluation with respect to specific learning schemes. This leads to a large number of possible permutations and has led to a situation where very few benchmark studies have been conducted.

      This paper presents a benchmark comparison of several attribute selection methods. All the methods produce an attribute ranking, a useful devise of isolating the individual merit of an attribute. Attribute selection is achieved by cross-validating the rankings with respect to a learning scheme to find the best attributes. Results are reported for a selection of standard data sets and two learning schemes C4.5 and naive Bayes.
      Date
      2000-07
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      00/10
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 2000 Working Papers [12]
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