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Cited by 269

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A further comparison of splitting rules for decision-tree induction

Open access
Buntine, Wray, Niblett, Tim·1992·Machine Learning

Abstract

One approach to learning classification rules from examples is to build decision trees. A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tree building, which uses a “splitting rule” to grow trees with a greedy recursive partitioning algorithm. That paper considered a number of different measures and experimentally examined their behavior on four domains. The main conclusion was that a random splitting rule does not significantly decrease classificational accuracy. This note suggests an alternative experimental method and presents additional results on further domains. Our results indicate that random splitting leads to increased error. These results are at variance with those presented by Mingers.

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Crossref · 10.1007/bf00994006

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Metadata

Authors
Buntine, W., & Niblett, T.
Venue
Machine Learning
Published
1992-01
Volume
8
Issue
1
Pages
75-85
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
OpenAlex
W1969223365
Cited by
269
References
3
FWCI
40.23
Type
journal article
Language
en
Open access
hybrid

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