2022 - Research.com Computer Science in Poland Leader Award
2019 - Fellow of the Institute for Operations Research and the Management Sciences (INFORMS)
2017 - IEEE Fellow For contributions to dominance-based rough set theory, robust ordinal regression and preference learning
2013 - Member of Academia Europaea
2013 - Polish Academy of Science
2005 - Prize of the Foundation for Polish Science - Nagroda Fundacji na rzecz Nauki Polskiej for developing a methodology for computer-aided decision-making based on incomplete data
1991 - EURO Gold Medal
Fellow of the International Federation for Information Processing (IFIP) for his work in the areas of dominance-based rough set theory and robust ordinal regression.
His primary areas of investigation include Rough set, Dominance-based rough set approach, Data mining, Decision rule and Set. In general Rough set, his work in Decision table is often linked to Context linking many areas of study. His Dominance-based rough set approach study is related to the wider topic of Artificial intelligence.
Roman Słowiński interconnects Machine learning and Decision matrix in the investigation of issues within Artificial intelligence. His Data mining study combines topics in areas such as Information system, Management information systems, Bayesian probability and Extension. His Decision rule research incorporates themes from Weighted sum model, Decision problem, Rule of inference and Linear discriminant analysis.
Rough set, Dominance-based rough set approach, Artificial intelligence, Decision rule and Data mining are his primary areas of study. His work carried out in the field of Rough set brings together such families of science as Fuzzy set, Set, Mathematical optimization, Algorithm and Monotonic function. His work on Multicriteria classification as part of general Dominance-based rough set approach research is frequently linked to Context, thereby connecting diverse disciplines of science.
Roman Słowiński has researched Artificial intelligence in several fields, including Ranking, Machine learning, Decision problem and Pattern recognition. His Decision rule study incorporates themes from Decision tree, Weighted sum model, Optimal decision, Multiple-criteria decision analysis and Decision analysis. The concepts of his Data mining study are interwoven with issues in Bayesian probability, Management information systems and Extension.
His primary scientific interests are in Rough set, Mathematical optimization, Dominance-based rough set approach, Artificial intelligence and Preference. Roman Słowiński undertakes multidisciplinary investigations into Rough set and Dominance in his work. His Mathematical optimization research is multidisciplinary, relying on both Set, Decision problem and Pairwise comparison.
Dominance-based rough set approach is the subject of his research, which falls under Data mining. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition. His Preference research is multidisciplinary, incorporating perspectives in Sorting, Ordinal regression and Bellman equation.
The scientist’s investigation covers issues in Mathematical optimization, Decision problem, Multiple-criteria decision analysis, Rough set and Artificial intelligence. His studies deal with areas such as Set and Pairwise comparison as well as Mathematical optimization. His Decision problem research integrates issues from Structure, Ordinal regression, Decision aiding and Hierarchy.
His biological study deals with issues like Management science, which deal with fields such as Decision support system, Structuring and Technical support. His Rough set research includes themes of Unification, Decision rule and Class information. As part of one scientific family, Roman Słowiński deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Machine learning, and often Data mining.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Rough sets
Zdzislaw Pawlak;Jerzy Grzymala-Busse;Roman Slowinski;Wojciech Ziarko.
Communications of The ACM (1995)
Rough sets theory for multicriteria decision analysis
Salvatore Greco;Benedetto Matarazzo;Roman Slowinski.
European Journal of Operational Research (2001)
Rough Sets and Current Trends in Computing
Salvatore Greco;Yutaka Hata;Shoji Hirano;Masahiro Inuiguchi.
Systems, control and information (1998)
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Roman Slowinski.
(1992)
A generalized definition of rough approximations based on similarity
R. Slowinski;D. Vanderpooten.
IEEE Transactions on Knowledge and Data Engineering (2000)
Intelligent Decision Support
Roman Słowiński.
(1992)
Multiobjective Optimization: Interactive and Evolutionary Approaches
Jürgen Branke;Kalyanmoy Deb;Kaisa Miettinen;Roman Słowiński.
(2008)
Business failure prediction using rough sets
A. I. Dimitras;Roman Slowinski;Robert Susmaga;Constantin Zopounidis.
European Journal of Operational Research (1999)
Rough approximation of a preference relation by dominance relations
Salvatore Greco;Benedetto Matarazzo;Roman Slowinski.
European Journal of Operational Research (1999)
Inferring an ELECTRE TRI Model from Assignment Examples
V. Mousseau;R. Slowinski.
Journal of Global Optimization (1998)
European Journal of Operational Research
(Impact Factor: 6.363)
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