ITEM METADATA RECORD
Title: Efficient algorithms for decision tree cross-validation
Authors: Blockeel, Hendrik
Struyf, Jan
Issue Date: Feb-2001
Publisher: Department of Computer Science, K.U.Leuven, Leuven, Belgium
Series Title: CW Reports vol:CW305 pages:1-10
Abstract: Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementations of the technique is their computational overhead. In this paper we show that, for decision trees, the computational overhead of cross-validation can be reduced significantly by integrating the cross-validation with the normal decision tree induction process. We discuss how existing decision tree algorithms can be adapted to this aim, and provide an analysis of the speedups these adaptations may yield. The analysis is supported by experimental results.
URI: 
Publication status: published
KU Leuven publication type: IR
Appears in Collections:Informatics Section

Files in This Item:
File Status SizeFormat
CW305.pdf Published 527KbAdobe PDFView/Open

 


All items in Lirias are protected by copyright, with all rights reserved.