The complexity of Boolean functions
The complexity of Boolean functions
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Rough approximation quality revisited
Artificial Intelligence
Approximation quality for sorting rules
Computational Statistics & Data Analysis
Machine Learning
Approximation quality for sorting rules
Computational Statistics & Data Analysis
Rough Relation Algebras Revisited
Fundamenta Informaticae
Learnability in rough set approaches
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Quality of rough approximation in multi-criteria classification problems
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Rough Relation Algebras Revisited
Fundamenta Informaticae
Evaluation of the decision performance of the decision rule set from an ordered decision table
Knowledge-Based Systems
A novel believable rough set approach for supplier selection
Expert Systems with Applications: An International Journal
| Hi-index | 0.03 |
A method is presented for the approximation quality for sorting rules, which is based on Rough Set Data Analysis. Several types of rules are discussed: The "nominal → nominal" case (NN) (the classical Rough Set approach), "nominal → ordinal" (NO) rules, and "ordinal → ordinal" (OO) rules, and its generalisation to "(nominal, ordinal) → ordinal" rules (NO-O). We provide a significance test for the overall approximation quality, and a test for partial influence of attributes based on the bootstrap technology.For the bivariate case, the relationship of U-statistics and the proposed approximation quality measure is discussed. It can be shown that in this case a simple linear transformation of the Kendall tau correlation forms an upper bound for the approximation quality of sorting rules.A competing model is also studied. Whereas this method is a promising tool in case of searching for global consistency, we demonstrate that in case of local perturbations in the data set the method may offer questionable results, and that it is dissociated from the theory it claims to support.In the final Section, an example illustrates the introduced concepts and procedures.