Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Variable Consistency Model of Dominance-Based Rough Sets Approach
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
Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization
Multiobjective Optimization
Rough Set Approach to Knowledge Discovery about Preferences
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Variable-precision dominance-based rough set approach
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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We introduce the concept of variable-consistency monotonic decision tree induced from preference-ordered data concerning a multicriteria sorting (classification) problem. Given the data in form of an information table including some sorting examples, we propose to induce a decision tree using an inductive learning algorithm. The decision tree can be considered as a preference model of a decision maker who supplied the sorting examples. Moreover, a partial violation of the dominance principle is admitted and controlled by an index called consistency level. The monotonic decision trees with variable consistency can be applied to a wide range of possible applications, for instance, financial rating, bank creditworthiness, medical diagnosis, and the like.