Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets
Tzung‐Pei Hong, Yan-Liang Liou, Shyue-Liang Wang · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2006
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.