A new version of the rule induction system LERS
Fundamenta Informaticae
Query approximate answering system for an incomplete DKBS
Fundamenta Informaticae - Special issue: intelligent information systems
On semantic issues connected with incomplete information databases
ACM Transactions on Database Systems (TODS)
Applying rough sets to data tables containing possibilistic information
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Rough sets handling missing values probabilistically interpreted
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Checking whether or not rough-set-based methods to incomplete data satisfy a correctness criterion
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Transactions on Rough Sets II
| Hi-index | 0.01 |
Rough set theory depending upon deterministic information systems or knowledge bases is now becoming a mathematical foundation of soft computing. In this paper, we pick up nondeterministic knowledge bases with incomplete and selective information. The both information are given as a set of attribute values, whose difference comes from the temporal concept. If the information is referring the past information then we see it incomplete information. On the other hand, selective information means that the real attribute value is not decided in a set, i.e., we can select the most proper value from this set. By introducing these two information into knowledge bases, we develop another framework for nondeterministic knowledge bases. Namely, we discuss question-answering, approximation, rough set concept and dependencies of attributes on this nondeterministic knowledge bases.