Fuzzy sets, decision making and expert systems
Fuzzy sets, decision making and expert systems
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Expert systems: knowledge, uncertainty, and decision
Expert systems: knowledge, uncertainty, and decision
Variable precision rough set model
Journal of Computer and System Sciences
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Data mining using extensions of the rough set model
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Incompleteness and Uncertainty in Information Systems
Incompleteness and Uncertainty in Information Systems
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Handling Various Types of Uncertainty in the Rough Set Approach
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Knowledge acquisition from quantitative data using the rough-set theory
Intelligent Data Analysis
Rough classification in incomplete information systems
Mathematical and Computer Modelling: An International Journal
Rough sets based association rules application for knowledge-based system design
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Expert Systems with Applications: An International Journal
Transversal and function matroidal structures of covering-based rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Multi knowledge based rough approximations and applications
Knowledge-Based Systems
Crisp and soft clustering of mobile calls
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Correlating Fuzzy and Rough Clustering
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
The Journal of Supercomputing
Hi-index | 12.05 |
Machine learning can extract desired knowledge from existing training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete data sets. If some attribute values are unknown in a data set, it is called incomplete. Learning from incomplete data sets is usually more difficult than learning from complete data sets. In the past, the rough-set theory was widely used in dealing with data classification problems. Most conventional mining algorithms based on the rough-set theory identify relationships among data using crisp attribute values. Data with quantitative values, however, are commonly seen in real-world applications. In this paper, we thus deal with the problem of learning from incomplete quantitative data sets based on rough sets. A learning algorithm is proposed, which can simultaneously derive certain and possible fuzzy rules from incomplete quantitative data sets and estimate the missing values in the learning process. Quantitative values are first transformed into fuzzy sets of linguistic terms using membership functions. Unknown attribute values are then assumed to be any possible linguistic terms and are gradually refined according to the fuzzy incomplete lower and upper approximations derived from the given quantitative training examples. The examples and the approximations then interact on each other to derive certain and possible rules and to estimate appropriate unknown values. The rules derived can then serve as knowledge concerning the incomplete quantitative data set.