Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets

Authors
Tzung-Pei Hong 0, Yan-Liang Liou, Shyue-Liang Wang
Corresponding Author
Tzung-Pei Hong
0National University of Kaohsiung
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.306How to use a DOI?
Keywords
machine learning, rough set, hierarchical value, quantitative value.
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.
Open Access
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.306How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Tzung-Pei Hong
AU  - Yan-Liang Liou
AU  - Shyue-Liang Wang
PY  - 2006/10
DA  - 2006/10
TI  - Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.306
DO  - https://doi.org/10.2991/jcis.2006.306
ID  - Hong2006/10
ER  -