Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets

DOI

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.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/jcis.2006.306
ID  - Hong2006/10
ER  -