title: |
Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-01-7 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/jcis.2006.306 (how to use a DOI) | |
author(s): |
Tzung-Pei Hong, Yan-Liang Liou, Shyue-Liang Wang |
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corresponding author: |
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publication date: |
October 2006 |
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keywords: |
machine learning, rough set, hierarchical value, quantitative value. |
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abstract: |
This paper proposes an approach to deal with the problem of producing a set of cross-level fuzzy certain and possible rules from examples with hierarchical and quantitative attributes. The proposed approach combines the rough-set theory and the fuzzy-set theory to learn. Some pruning heuristics are adopted in the proposed algorithm to avoid unnecessary search. A simple example is also given to illustrate the proposed approach. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |