The problem of missing values in decision tree grafting

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Abstract

Decision tree grafting adds nodes to inferred decision trees. Previous research has demonstrated that appropriate grafting techniques can improve predictive accuracy across a wide cross-selection of domains. However, previous decision tree grafting systems are demonstrated to have a serious deficiency for some data sets containing missing values. This problem arises due to the method for handling missing values employed by C4.5, in which the grafting systems have been embedded. This paper provides an explanation of and solution to the problem. Experimental evidence is presented of the efficacy of this solution.

Original languageEnglish
Title of host publicationAdvanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers
EditorsGrigoris Antoniou, John Slaney
PublisherSpringer
Pages273-283
Number of pages11
ISBN (Print)3540651381, 9783540651383
Publication statusPublished - 1 Jan 1998
Externally publishedYes
EventAustralasian Joint Conference on Artificial Intelligence 1998 - Brisbane, Australia
Duration: 13 Jul 199817 Jul 1998
Conference number: 11th
https://link.springer.com/book/10.1007/BFb0095035 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1502
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAustralasian Joint Conference on Artificial Intelligence 1998
Abbreviated titleAI 1998
CountryAustralia
CityBrisbane
Period13/07/9817/07/98
Internet address

Keywords

  • Decision tree learning
  • Grafting
  • Missing values

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