An Empirical Comparison of Selection Measures for Decision-Tree Induction

Mingers, John (1989) An Empirical Comparison of Selection Measures for Decision-Tree Induction. Machine Learning, 3 (4). pp. 319-342. ISSN 0885-6125 . (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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One approach to induction is to develop a decision tree from a set of examples. When used with noisy rather than deterministic data, the method involve-three main stages—creating a complete tree able to classify all the examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper is concerned with the first stage — tree creation which relies on a measure for goodness of split, that is, how well the attributes discriminate between classes. Some problems encountered at this stage are missing data and multi-valued attributes. The paper considers a number of different measures and experimentally examines their behavior in four domains. The results show that the choice of measure affects the size of a tree but not its accuracy, which remains the same even when attributes are selected randomly.

Item Type: Article
Uncontrolled keywords: Decision trees - knowledge acquisition - induction - noisy data
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD29 Operational Research - Applications
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: John Mingers
Date Deposited: 09 Sep 2009 19:20
Last Modified: 02 Jun 2014 10:26
Resource URI: (The current URI for this page, for reference purposes)
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