Boosting the Performance of Nearest Neighbour Methods with Feature Selection

Geva, Shlomo (2001) Boosting the Performance of Nearest Neighbour Methods with Feature Selection. In 5th Pacific-Asia Conference PAKDD 2001 : Advances in Knowledge Discovery and Data Mining, 16-18 April 2001, Hong Kong, China.

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This paper describes a Nearest Neighbour procedure for variable selection in function approximation, pattern classification, and time series prediction. Given a training set of input/output vector pairs the procedure identifies a subset of input vector components that effectively capture the input-output relationship implicit in the training set. The utility of this procedure is demonstrated with numerous data sets from the UCI repository of machine learning databases and the Mackey-Glass time series prediction. A comprehensive set of benchmark problems is used to demonstrate comparable performance to that of much more complex boosted C4.5 decision trees.

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1 citations in Scopus
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ID Code: 9665
Item Type: Conference Paper
Refereed: Yes
Additional Information: For more information, please refer to the publisher’s website (see hypertext link) or contact the author.
DOI: 10.1007/3-540-45357-1_25
ISBN: 3540419101
ISSN: 1611-3349
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2001 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. Lecture Notes in Computer Science
Deposited On: 21 Sep 2007 00:00
Last Modified: 15 Jan 2009 07:46

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