Handling continuous-valued attributes in decision tree with neural network modeling

Dae Eun Kim, Jaeho Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


Induction tree is useful to obtain a proper set of rules for a large amount of examples. However, it has difficulty in obtaining the relation between continuous-valued data points. Many data sets show significant correlations between input variables, and a large amount of useful information is hidden in the data as nonlinearities. It has been shown that neural network is better than direct application of induction tree in modeling nonlinear characteristics of sample data. It is proposed in this paper that we derive a compact set of rules to support data with input variable relations. Those relations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. This will also solve overgeneralization amd overspecialization problems often seen in induction tree. We have tested this scheme over several data sets to compare with decision tree results.

Original languageEnglish
Title of host publicationMachine Learning
Subtitle of host publicationECML 2000 - 11th European Conference on Machine Learning, Proceedings
EditorsRamon Lopez de Mantaras, Enric Plaza
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783540451648
Publication statusPublished - 2000
Event11th European Conference on Machine Learning, ECML 2000 - Barcelona, Catalonia, Spain
Duration: 2000 May 312000 Jun 2

Publication series

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


Other11th European Conference on Machine Learning, ECML 2000
CityBarcelona, Catalonia

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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