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                                       Details for article 6 of 13 found articles
 
 
  Induction of decision trees in numeric domains using set-valued attributes
 
 
Title: Induction of decision trees in numeric domains using set-valued attributes
Author: Dimitrios Kalles
Athanasios Papagelis
Eirini Ntoutsi
Appeared in: Intelligent data analysis
Paging: Volume 4 (2001) nr. 3-4 pages 323-347
Year: 2001-04-01
Contents: Conventional algorithms for decision tree induction use an attribute-value representation scheme for instances. This paper explores the empirical consequences of using set-valued attributes. This simple representational extension, when used as a pre-processor for numeric data, is shown to yield significant gains in accuracy combined with attractive build times. It is also shown to improve the accuracy for the second best classification option, which has valuable ramifications for post-processing. To do so an intuitive and practical version of pre-pruning is employed. Moreover, the implementation of a simple pruning scheme serves as an example of pruning applicability over the resulted trees and also as an indication that the proposed discretization absorbs much of pruning potential. Finally, we construct several versions of the basic algorithm to examine the value of every component that comprises it.
Publisher: IOS Press
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details for article 6 of 13 found articles
 
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