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Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In(More)
We generalize the notion of an approximation space introduced in 8]. In tolerance approximation spaces we deene the lower and upper set approximations. We investigate some attribute reduction problems for tolerance approximation spaces determined by tolerance information systems. The tolerance relation deened by the so called uncertainty function or the(More)
We present applications of rough set methods for feature selection in pattern recognition. We emphasize the role of the basic constructs of rough set approach in feature selection, namely reducts and their approximations, including dynamic reducts. In the overview of methods for feature selection we discuss feature selection criteria, including the rough(More)
We are concerned with formal models of reasoning under uncertainty. Many approaches to this problem are known in the literature e.g. Dempster-Shafer theory, bayesian-based reasoning, belief networks, fuzzy logics etc. We propose rough mere-ology as a foundation for approximate reasoning about complex objects. Our notion of a complex object includes(More)
s. The quantization of real value attributes is one of the main problem to be solved in synthesis of decision rules from data tables with real value attributes. We present an approach to this problem based on rough set methods and Boolean reasoning. The main result states that the problem of optimal quantization of real value attributes is polynomially(More)
A rapid growth of interest in rough set theory 290] and its applications can be lately seen in the number of international workshops, conferences and seminars that are either directly dedicated to rough sets, include the subject in their programs, or simply accept papers that use this approach to solve problems at hand. A large number of high quality papers(More)
We apply rough set methods and boolean reasoning for knowledge discovery from decision tables. It is not always possible to extract general laws from experimental data by computing rst all reducts 12] of a decision table and next decision rules on the basis of these reducts. We investigate a problem how information about the reduct set changes in a random(More)