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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. (doi:10.1007/BF00116837) (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) (KAR id:3785)

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.
Official URL:
http://dx.doi.org/10.1007/BF00116837

Abstract

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
DOI/Identification number: 10.1007/BF00116837
Uncontrolled keywords: Decision trees - knowledge acquisition - induction - noisy data
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD29 Operational Research - Applications
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: John Mingers
Date Deposited: 09 Sep 2009 19:20 UTC
Last Modified: 16 Nov 2021 09:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/3785 (The current URI for this page, for reference purposes)

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