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  • Articles: DFG German National Licenses  (2)
  • noise  (2)
  • 1
    Electronic Resource
    Electronic Resource
    Bias in information-based measures in decision tree induction (1994)
    Springer
    Machine learning 15 (1994), S. 321-329 
    Details
    ISSN: 0885-6125
    Keywords: Decision trees ; noise ; induction ; unbiased attribute selection ; information-based measures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A fresh look is taken at the problem of bias in information-based attribute selection measures, used in the induction of decision trees. The approach uses statistical simulation techniques to demonstrate that the usual measures such as information gain, gain ratio, and a new measure recently proposed by Lopez de Mantaras (1991) are all biased in favour of attributes with large numbers of values. It is concluded that approaches which utilise the chi-square distribution are preferable because they compensate automatically for differences between attributes in the number of levels they take.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993349
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Technical Note: Bias in Information-Based Measures in Decision Tree Induction (1994)
    Springer
    Machine learning 15 (1994), S. 321-329 
    Details
    ISSN: 0885-6125
    Keywords: Decision trees ; noise ; induction ; unbiased attribute selection ; information-based measures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A fresh look is taken at the problem of bias in information-based attribute selection measures, used in the induction of decision trees. The approach uses statistical simulation techniques to demonstrate that the usual measures such as information gain, gain ratio, and a new measure recently proposed by Lopez de Mantaras (1991) are all biased in favour of attributes with large numbers of values. It is concluded that approaches which utilise the chi-square distribution are preferable because they compensate automatically for differences between attributes in the number of levels they take.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022694010754
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
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