Showing papers by "Zdzisław Pawlak published in 2000"
TL;DR: It is shown that every decision algorithm reveals some well known probabilistic properties, in particular it satisfies the Total Probability Theorem and the Bayes' Theorem, which give a new method of drawing conclusions from data, without referring to prior and posterior probabilities.
Abstract: Rough set based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm can be associated. It is shown that every decision algorithm reveals some well known probabilistic properties, in particular it satisfies the Total Probability Theorem and the Bayes' Theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.
51 citations
TL;DR: Basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined and some may be of extreme importance in the future are outlined.
Abstract: Application of intelligent methods in industry become a very challenging issue nowadays and will be of extreme importance in the future Intelligent methods include fuzzy sets, neural networks, genetics algorithms, and other techniques known as soft computing No doubt, rough set theory can also contribute essentially to this domain In this paper, basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined
47 citations
TL;DR: In this paper basic concepts of rough set theory will be given and its significance for decision analysis will be briefly discussed.
Abstract: Rough set theory is a new mathematical approach to vagueness and uncertainty. The theory has found many real life applications world wide. It is also considered as a very well suited new mathematical tool to deal with various decision problems and many papers on rough set theory and decision support have been published recently. Rough set theory gives new insight into the decision process and offers new efficient algorithms. Several real life decision problems have been successfully solved using this approach. In this paper basic concepts of rough set theory will be given and its significance for decision analysis will be briefly discussed.
43 citations
01 Jan 2000
TL;DR: Rough Measures and Integrals: A Brief Introduction, (with, J. Peters, A. Suraj, S. Ramanna and M. Borkowski)
Abstract: W: Bulletin of International Rough Set Society, 5(1-2):177-183, 2000 (S Hirano, M Inuiguchi, S Tsumoto (eds), Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC 2001), Matshue, Shimane, Japan, 2001); (see also: Rough Measures and Integrals: A Brief Introduction, (with, J Peters, A Skowron, Z Suraj, S Ramanna and M Borkowski), in: T Terano, T Nishida, A Namatame, S Tsumoto, Y Ohsawa and T Washio (eds), New Frontiers in Artificial Intelligence, LNAI 2253, Springer-Verlag, Berlin, 2001, 375-379)
25 citations
01 Dec 2000
TL;DR: Basic ideas of rough set theory were proposed by Zdzislaw Pawlak in the early 1980s and in the ensuing years, a systematic, world-wide growth of interest in rough sets and their applications was witnessed.
Abstract: Basic ideas of rough set theory were proposed by Zdzislaw Pawlak [90, 91] in the early 1980’s. In the ensuing years, we have witnessed a systematic, world-wide growth of interest in rough sets and their applications. There are numerous areas of successful applications of rough set software systems [101]. Many interesting case studies are reported (for references see e.g., [100, 101], [87] and the bibliography in these books, in particular [19], [46], [57], [132], [1461).
19 citations
01 Jan 2000
TL;DR: It is shown that every decision algorithm reveals some well known probabilistic properties, in particular it satisfies the total probability theorem and the Bayes’ theorem, giving a new method of drawing conclusions from data, without referring to prior and posterior probabilities.
Abstract: Rough set based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm can be associated. It is shown that every decision algorithm reveals some well known probabilistic properties, in particular it satisfies the total probability theorem and the Bayes’ theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.