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...

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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
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Expert systems
Inductive learning
Machine learning
Multi-dimensional decision tree
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description 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.
doi_str_mv 10.1080/09528130500281828
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source Business Source Ultimate; Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list)
subjects Expert systems
Inductive learning
Machine learning
Multi-dimensional decision tree
title MFILM: a multi-dimensional fuzzy inductive learning method
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