Induction of ordinal classification rules from decision tables with unknown monotonicity
We propose the dominance and fuzzy preference inconsistency rates. These indices can be used to discover global monotonicity in decision tables. Sometimes, the transformation of cloning all ordinal attributes is overcomplex. We propose a refined transformation method to avoid the issue of over-fitting.
inconsistency rates rough sets multiple criteria decision aiding dominance relations