Gradual inference rules in approximate reasoning
Information Sciences: an International Journal
Case-based reasoning
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Rough membership and bayesian confirmation measures for parameterized rough sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Generalizing rough set theory through dominance-based rough set approach
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Dominance-Based Rough Set Approach to Reasoning About Ordinal Data
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Credible rules in incomplete decision system based on descriptors
Knowledge-Based Systems
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Dominance-based rough set approach to incomplete interval-valued information system
Data & Knowledge Engineering
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
Dominance-based rough set approach to reasoning about ordinal data: a tutorial
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Case-based reasoning using gradual rules induced from dominance-based rough approximations
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Discovering rules-based similarity in microarray data
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Dominance-based fuzzy rough approach to an interval-valued decision system
Frontiers of Computer Science in China
Case-based reasoning using dominance-based decision rules
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Satisfiability judgement under incomplete information
Transactions on Rough Sets XI
Dynamic rule-based similarity model for DNA microarray data
Transactions on Rough Sets XV
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Information Sciences: an International Journal
Hi-index | 0.00 |
Case-based reasoning is a paradigm in machine learning whose idea is that a new problem can be solved by noticing its similarity to a set of problems previously solved. We propose a new approach to case-based reasoning. It is based on rough set theory that is a mathematical theory for reasoning about data. More precisely, we adopt Dominance-based Rough Set Approach (DRSA) that is particularly appropriate in this context for its ability of handling monotonicity relationship between ordinal properties of data related to monotonic relationships between attribute values in the considered data set. In general terms, monotonicity concerns relationship between different aspects of a phenomenon described by data: for example, “the larger the house, the higher its price” or “the closer the house to the city centre, the higher its price”. In the perspective of case-based reasoning, we propose to consider monotonicity of the type “the more similar is y to x, the more credible is that y belongs to the same set as x”. We show that rough approximations and decision rules induced from these approximations can be redefined in this context and that they satisfy the same fundamental properties of classical rough set theory.