Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets
Tzung-Pei Hong 0, Yan-Liang Liou, Shyue-Liang Wang
0National University of Kaohsiung
Available Online October 2006.
- https://doi.org/10.2991/jcis.2006.306How to use a DOI?
- machine learning, rough set, hierarchical value, quantitative value.
- 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
- 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 -