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)
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 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)
In this article, we present some extensions of the rough set approach and we outline a challenge for the rough set based research. The central problem of our age is how to act decisively in the absence of certainty.
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)
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 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)
In this article, we discuss methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis.
| An importance of the idea of granularity of knowledge for approximate reasoning has been recently stressed in 6,9-10]. We address here the problem of synthesis of adaptive decision algorithms and we propose an approach to this problem based on the notion of a granule which we develop in the framework of rough mereology. This framework does encompass both… (More)