MFILM: a multi-dimensional fuzzy inductive learning method

Inductive learning that creates a decision tree from a set of existing examples is shown to be useful for automated knowledge acquisition. Most of the existing methods however, handle only single-dimensional decision problems. Only some methods can deal with multi-dimensional decision problems. Howe...

Full description

Saved in:
Bibliographic Details
Published in:Journal of experimental & theoretical artificial intelligence 2005-09, Vol.17 (3), p.267-281
Main Authors: Chen, Yao-Tsung, Jeng, Bingchiang
Format: Article
Language:English
Subjects:
Expert systems
Inductive learning
Machine learning
Multi-dimensional decision tree
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Inductive learning that creates a decision tree from a set of existing examples is shown to be useful for automated knowledge acquisition. Most of the existing methods however, handle only single-dimensional decision problems. Only some methods can deal with multi-dimensional decision problems. However, they are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a multi-dimensional fuzzy inductive learning method that integrates the fuzzy set theory into the conventional multi-dimensional decision tree induction methods. The method converts a multi-dimensional decision tree into a fuzzy multi-dimensional decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy. Results from empirical tests indicate that the new fuzzy approach outperforms the other conventional methods.
ISSN:0952-813X
1362-3079
DOI:10.1080/09528130500281828