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.
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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|Item Type:||Conference Paper|
|Additional Information:||For more information, please refer to the publisher’s website (see hypertext link) or contact the author.|
|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.
http://www.springer.de/comp/lncs/ Lecture Notes in Computer Science
|Deposited On:||21 Sep 2007 00:00|
|Last Modified:||15 Jan 2009 07:46|
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