Zdzislaw Pawlak
Abstract
Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
- 1 Dubois, D., and Prade, H. Foreword. Rough Sets: Theoretical Aspects of Reasoning about Data, by Z. Pawlak. Kluwer, Dordrecht, Netherlands, 1991.Google Scholar
- 2 Grzymala-Busse, J. W. Knowledge acquisition under uncertainty--A rough set approach.J, lntel. Rob. Syst. 1, 1 (1988), 3-16.Google Scholar
- 3 Grzymala-Busse, J. W. Managing Uncertainty in Expert Systems. Kluwer, Dordrecht, Netherlands, 1991. Google ScholarDigital Library
- 4 Grzymala-Busse, J. W. LERS--A system for learning from examples based on rough sets. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. R. Slowinski, Ed. Kluwer, Dordrecht, Boston, London, pp. 1992, 3-18.Google Scholar
- 5 Grzymala-Busse, D. M., and Grzymala-Busse, J. W. Evaluation of machine learning approach to knowledge acquisition. In Proceedings of the 14th International Avignon Conference. (Paris, May 30-June 3). 1994, pp. 183-192.Google Scholar
- 6 Gunn, J. D., and Grzymala-Busse, J. W. Global temperature stability by rule induction: An interdisciplinary bridge. Hum. Ecol. 22 (1994), 59-81.Google ScholarCross Ref
- 7 Krusinska, E., Slowinski, R., and Stefanowski, J. Discriminant versus rough set approach to vague data analysis. Appl. Stochastic Models and Data Anal. 8 (1992), 43-56.Google ScholarCross Ref
- 8 Krysinski, J. Rough set approach to the analysis of the structureactivity relationship of quaternary imidazolium compounds. Arzneimittel-Forschung, (Drug Research) 40, 7 (1990), 795-799.Google Scholar
- 9 Lin, T. Y., Ed. Proceedings of the t~%~C'95, Third International Workshop on Rough Sets and Soft Computing (San Jose, Calif. ) 1994.Google Scholar
- 10 Nowicki, R., Slowinski, R., and Stefanowski, J. Evaluation of vibroacoustic diagnostic symptoms by means of the rough set theory. Comput. lnd. 20 (1992), 141-152. Google ScholarDigital Library
- 11 Pawlak, Z. Rough sets. lnt.J. Comput. lnf Sci. 11 (1982), 341-356.Google Scholar
- 12 Pawlak, Z. Rough sets. In Theoretical Aspects of Reasoning About Data. Kluwer, Netherlands, 1991. Google ScholarDigital Library
- 13 Pawlak, Z., and Skowron, A. Rough membership functions. In R. R. Yaeger, M. Fedrizzi, andJ. Kacprzyk, Eds. Advances in the Dempster-Shafer Theory of Evidence. Wiley, New York, 1994, 251-271. Google ScholarDigital Library
- 14 Pawlak, Z., and Slowinski, R. Rough set approach to multiattribute decision analysis. Invited Review, Eur. J. of Oper. Res. 72 (1994), 443-459.Google ScholarCross Ref
- 15 Skowron, A., and Grzymala-Busse, J. W. From rough set theory to evidence theory. In Advances in the Dempster-Shafer Theory of Evidence. R. R. Yaeger, M. Fedrizzi, andJ. Kacprzyk, Eds. Wiley, New York, 1994, 193-236. Google ScholarDigital Library
- 16 Skowron, A., and Rauszer, C. The discernibility matrices and functions in information systems. Intelligent Decision Support. Handbook of Advances and Applications of the Rough Set Theory. R. Slowinski, Ed. Kluwer, Dordrecht, Netherlands, 1992, 331-362.Google Scholar
- 17 Slowinski, K. Rough classification of HSV patients. In Intelligent Decision Support. Handbook of Advances and Applications of the Rough Set Theory, R. Slowinski, Ed. Kluwer, Dordrecht, Netherlands, 1992, 77-94.Google Scholar
- 18 Slowinski, R., Ed. Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Kluwer, Dordrecht, Netherlands, 1992. Google ScholarDigital Library
- 19 Slowinski, R., and Zopounidis, C. Rough set sorting of firms according to bankruptcy risk. In Applying Multiple Criteria Aid for Decision to Environmental Management. M. Paruccini , Ed. Kluwer, Dordrecht, Netherlands, 1994, 339-357.Google Scholar
- 20 Szladow, A. Datalogic/R: Mining the knowledge in databases. PC A1 7, 1 (1993), 40-41.Google Scholar
- 21 Szladow, A., and Ziarko, W. Rough sets: Working with imperfect data. AIExpert 7 (1993), 36-41.Google Scholar
- 22 Ziarko, W. The discovery, analysis and representation of data dependencies in databases. Knowledge Discovery in Databases, G. Piatetsky-Shapiro, and W.J. Frawley, Eds. AAAI Press/MIT Press, 1991, 177-195.Google Scholar
- 23 Ziarko, W. Variable precision rough sets model. J. Comput. Syst. Scie. 46 (1993), 39-59. Google ScholarDigital Library
- 24 Ziarko, W., Ed. Rough sets, fuzzy sets and knowledge discovery. In Proceedings of t~KD'94 Workshop (Banff). Springer-Verlag, Berlin, 1994. Google ScholarDigital Library
- 25 Ziarko, W., Golan, R., and Edwards, D. An application of datalogic/R knowledge discovery tool to identify strong predictive rules in stock market data. In Proceedings of AAA1 Workshop on Knowledge Discovery in Databases, (Washington, D.C.). 1993, 89-101.Google Scholar
Index Terms
- Rough sets
Recommendations
Relationships between covering-based rough sets and relation-based rough sets
Rough set theory is an important technique to deal with vagueness and granularity in information systems. In rough set theory, relation-based rough sets and covering-based rough sets are two important extensions of the classical rough sets. This paper ...
Soft rough fuzzy sets and soft fuzzy rough sets
Fuzzy set theory, soft set theory and rough set theory are mathematical tools for dealing with uncertainties and are closely related. Feng et al. introduced the notions of rough soft set, soft rough set and soft rough fuzzy set by combining fuzzy set, ...
Multigranulation decision-theoretic rough sets
The Bayesian decision-theoretic rough sets propose a framework for studying rough set approximations using probabilistic theory, which can interprete the parameters from existing forms of probabilistic approaches to rough sets. Exploring rough sets in ...
Comments