Variable precision rough set model
Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Measuring Customer Satisfaction Using a Collective PreferenceDisaggregation Model
Journal of Global Optimization
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Rough Set Analysis of Preference-Ordered Data
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Variable Consistency Monotonic Decision Trees
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Statistical Model for Rough Set Approach to Multicriteria Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
Analyzing IT business values - A Dominance based Rough Sets Approach perspective
Expert Systems with Applications: An International Journal
An improved variable precision model of dominance-based rough set approach
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Optimal sub-reducts with test cost constraint
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Case-based reasoning using dominance-based decision rules
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A new method for inconsistent multicriteria classification
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Inductive discovery of laws using monotonic rules
Engineering Applications of Artificial Intelligence
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
On variable consistency dominance-based rough set approaches
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Variable-precision dominance-based rough set approach
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Rough set approach to customer satisfaction analysis
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Credit scoring analysis using a fuzzy probabilistic rough set model
Computational Statistics & Data Analysis
Incremental versus non-incremental rule induction for multicriteria classification
Transactions on Rough Sets II
Attribute reduction of data with error ranges and test costs
Information Sciences: an International Journal
Dominance-Based Rough Sets Using Indexed Blocks as Granules
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
Fundamenta Informaticae - Fundamentals of Knowledge Technology
A new intuitionistic fuzzy rough set approach for decision support
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Hyperplane Aggregation of Dominance Decision Rules
Fundamenta Informaticae - International Conference on Soft Computing and Distributed Processing (SCDP'2002)
Information Sciences: an International Journal
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Consideration of preference-orders requires the use of an extended rough set model called Dominance-based Rough Set Approach (DRSA). The rough approximations defined within DRSA are based on consistency in the sense of dominance principle. It requires that objects having not-worse evaluation with respect to a set of considered criteria than a referent object cannot be assigned to a worse class than the referent object. However, some inconsistencies may decrease the cardinality of lower approximations to such an extent that it is impossible to discover strong patterns in the data, particularly when data sets are large. Thus, a relaxation of the strict dominance principle is worthwhile. The relaxation introduced in this paper to the DRSA model admits some inconsistent objects to the lower approximations; the range of this relaxation is controlled by an index called consistency level. The resulting model is called variable-consistency model (VC-DRSA). We concentrate on the new definitions of rough approximations and their properties, and we propose a new syntax of decision rules characterized by a confidence degree not less than the consistency level. The use of VC-DRSA is illustrated by an example of customer satisfaction analysis referring to an airline company.