International Journal of Man-Machine Studies
Rough classification of patients after highly selective vagotomy for duodenal ulcer
International Journal of Man-Machine Studies
Attribute reduction based on evidence theory in incomplete decision systems
Information Sciences: an International Journal
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Expert Systems with Applications: An International Journal
Spatially enabled customer segmentation using a data classification method with uncertain predicates
Decision Support Systems
Mining from incomplete quantitative data by fuzzy rough sets
Expert Systems with Applications: An International Journal
Incomplete information system andits optimal selections
Computers & Mathematics with Applications
Set-valued information systems
Information Sciences: an International Journal
An Investigation About Rough Set Theory: Some Foundational and Mathematical Aspects
Fundamenta Informaticae - Advances in Rough Set Theory
Flexible Indiscernibility Relations for Missing Attribute Values
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Rough-Set Reasoning About Uncertain Data
Fundamenta Informaticae
An Improved Axiomatic Definition of Information Granulation
Fundamenta Informaticae
A novel feature selection method and its application
Journal of Intelligent Information Systems
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The paper is concerned with the problems of rough sets theory and rough classification of objects. It is a new approach to problems from the field of decision-making, data analysis, knowledge representation, expert systems etc. Several applications (particularly in medical diagnosis and engineering control) confirm the usefulness of the rough sets idea. Rough classification concerns objects described by multiple attributes in a so-called information system. Traditionally, the information system is assumed to be complete, i.e. the descriptors are not missing and are supposed to be precise. In this paper we investigate the case of incomplete information systems, and present a generalization of the rough sets approach which deals with missing and imprecise descriptors.