A statistical-heuristic feature selection criterion for decisiontree induction
Zhou, X.J.
Dillon, T.S.
Dept. of Comput Sci., La Trobe Univ., Bundoora, Vic.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 1991
Volume: 13,
Issue: 8
On page(s): 834-841
ISSN: 0162-8828
References Cited: 30
CODEN: ITPIDJ
INSPEC Accession Number: 4034327
Digital Object Identifier: 10.1109/34.85676
Current Version Published: 2002-08-06
Abstract
The authors present a statistical-heuristic feature selection
criterion for constructing multibranching decision trees in noisy
real-world domains. Real world problems often have multivalued features.
To these problems, multibranching decision trees provide a more
efficient and more comprehensible solution that binary decision trees.
The authors propose a statistical-heuristic criterion, the symmetrical
τ and then discuss its consistency with a Bayesian classifier and
its built-in statistical test. The combination of a measure of
proportional-reduction-in-error and cost-of-complexity heuristic enables
the symmetrical τ to be a powerful criterion with many merits,
including robustness to noise, fairness to multivalued features, and
ability to handle a Boolean combination of logical features, and
middle-cut preference. The τ criterion also provides a natural basis
for prepruning and dynamic error estimation. Illustrative examples are
also presented
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