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Rudiments of rough sets
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“Some systems integrate basic learning capabilities [72] through feedback loops [73,74], heuristics [75], or updated context buffers to refine behavior over time, particularly in settings like personalized recommendations or conversation flow management [7678].”
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Items 201–352 of 352
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Wróblewski, J. (2004) · Cognitive technologies · doi:10.1007/978-3-642-18859-6_6 · Cited by 38
Pre-topologies and Dynamic Spaces
Pagliani, P. (2007) · Springer eBooks · doi:10.1007/3-540-39205-x_19 · Cited by 38
Feature subset selection for classification of histological images
Jelonek, J., & Stefanowski, J. (1997) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(96)00375-2 · Cited by 38
Feature Selection with Rough Sets for Web Page Classification
An, A., Huang, Y., Huang, X., et al. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27778-1_1 · Cited by 38
Application of Temporal Descriptors to Musical Instrument Sound Recognition
Wieczorkowska, A., Wróblewski, J., Synak, P., et al. (2003) · Journal of Intelligent Information Systems · doi:10.1023/a:1023505917953 · Cited by 37
Pawlak, Z. (2005) · Lecture notes in computer science · doi:10.1007/11574798_1 · Cited by 37
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Wakulicz–Deja, A., & Paszek, P. (2002) · Fundamenta Informaticae · Cited by 37
Data mining and rough set theory
Grzymala‐Busse, J. W., & Ziarko, W. (2000) · Communications of the ACM · doi:10.1145/332051.332082 · Cited by 37
Classification of meteorological volumetric radar data using rough set methods
Peters, J. F., Suraj, Z., Shan, S., Ramanna, S., Witold Pedrycz, C., & Pizzi, N. J. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00203-9 · Cited by 37
Incremental versus Non-incremental Rule Induction for Multicriteria Classification
Greco, S., Słowiński, R., Stefanowski, J., & Żurawski, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27778-1_3 · Cited by 36
Tsumoto, S. (2009) · Encyclopedia of Complexity and Systems Science · doi:10.1007/978-0-387-30440-3_459 · Cited by 36
On decomposition for incomplete data
Latkowski, R. (2002) · Cited by 36
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Nguyen, H. S., & Nguyen, S. H. (1999) · Fundamenta Informaticae · doi:10.3233/fi-1999-40403 · Cited by 35
Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning
Polkowski, L., & Skowron, A. (2000) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1840-6_4 · Cited by 34
Self-citationWoleński, J. (1994) · Springer eBooks · doi:10.1007/978-94-015-8273-5 · Cited by 34
Variable Precision Bayesian Rough Set Model and Its Application to Human Evaluation Data
Nishino, T., Nagamachi, M., & Tanaka, H. (2005) · Lecture notes in computer science · doi:10.1007/11548669_31 · Cited by 34
Pal, S. K., Dasgupta, B., & Mitra, P. (2004) · Applied Intelligence · doi:10.1023/b:apin.0000043561.99513.69 · Cited by 34
Diagnose progressive encephalopathy applying the rough set theory
Wakulicz–Deja, A., & Paszek, P. (1997) · International Journal of Medical Informatics · doi:10.1016/s1386-5056(97)00061-0 · Cited by 34
Wasilewska, A. (1997) · Springer eBooks · doi:10.1007/978-1-4613-1461-5_21 · Cited by 34
Nominal and verbal semantic structure
Filip, H. (2001) · Language Sciences · doi:10.1016/s0388-0001(00)00033-4 · Cited by 33
Rough Set Approach to the Survival Analysis
Bazan, J. G., Osmólski, A., Skowron, A., et al. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45813-1_69 · Cited by 33
Self-citationPresenting the special issue on Rough-neuro computing
Pal, S. K., Pedrycz, W., Skowron, A., et al. (2001) · Neurocomputing · doi:10.1016/s0925-2312(00)00332-5 · Cited by 32
Self-citationGrzymala‐Busse, J. W. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_65 · Cited by 32
Rough Set Approach to Domain Knowledge Approximation
Nguyen, T. T., & Skowron, A. (2007) · Springer eBooks · doi:10.1007/3-540-39205-x_28 · Cited by 32
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Kryszkiewicz, M. (1997) · Springer eBooks · doi:10.1007/978-1-4613-1461-5_18 · Cited by 32
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Skowron, A. (2001) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1823-9_5 · Cited by 32
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Skowron, A., Stepaniuk, J., & Peters, J. F. (2002) · Fundamenta Informaticae · Cited by 32
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Nørsett, K. G., Lægreid, A., Midelfart, H., et al. (2004) · Cancer Letters · doi:10.1016/j.canlet.2004.01.022 · Cited by 32
Mitra, S. (2003) · Cited by 32
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Novotný, M., & Pawlak, Z. (1991) · Fundamenta Informaticae · doi:10.3233/fi-1991-14404 · Cited by 31
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Czyżewski, A. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00204-0 · Cited by 31
Skowron, A., & Synak, P. (2003) · Cited by 31
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Pedrycz, W., Han, L., Peters, J. F., et al. (2001) · Neurocomputing · doi:10.1016/s0925-2312(00)00340-4 · Cited by 30
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Grzymala‐Busse, J. W., & Grzymała-Busse, W. J. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_3 · Cited by 30
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Swift, J. J. (1885) · Cited by 29
A comparative study of some generalized rough approximations
Gomolińska, A. (2002) · Fundamenta Informaticae · Cited by 29
Dominance-Based Rough Set Approach to Knowledge Discovery (I): General Perspective
Greco, S., Matarazzo, B., & Słowiński, R. (2004) · Springer eBooks · doi:10.1007/978-3-662-07952-2_20 · Cited by 29
Roughian: Rough information analysis
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Applications of Rough Genetic Algorithms
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Stefanowski, J., & Wilk, S. (2001) · Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management · doi:10.1002/isaf.197 · Cited by 28
Investigation on AQ11, ID3 and the principle of discernibility matrix
Wang, J., Cui, J. J., & Zhao, K. (2001) · Journal of Computer Science and Technology · doi:10.1007/bf02948848 · Cited by 28
Flexible Indiscernibility Relations for Missing Attribute Values
Latkowski, R. (2005) · Fundamenta Informaticae · Cited by 27
Discovering Maximal Generalized Decision Rules Through Horizontal and Vertical Data Reduction
Hu, X. T., & Cercone, N. J. (2001) · Computational Intelligence · doi:10.1111/0824-7935.00169 · Cited by 27
A Note on 3-valued Rough Logic Accepting Decision Rules
Polkowski, L. (2004) · Fundamenta Informaticae · doi:10.3233/fun-2004-61104 · Cited by 26
On the Decision Table with Maximal Number of Reducts
Nguyen, H. S. (2003) · Electronic Notes in Theoretical Computer Science · doi:10.1016/s1571-0661(04)80718-7 · Cited by 26
A New Proposal for Fuzzy Rough Approximations and Gradual Decision Rule Representation
Greco, S., Inuiguchi, M., & Słowiński, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27778-1_16 · Cited by 25
Towards Scalable Algorithms for Discovering Rough Set Reducts
Kryszkiewicz, M., & Cichoń, K. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27794-1_5 · Cited by 25
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Pawlak, Z. (1997) · Springer eBooks · doi:10.1007/978-1-4613-1461-5_8 · Cited by 25
Self-citationOn-Line Elimination of Non-relevant Parts of Complex Objects in Behavioral Pattern Identification
Bazan, J. G., & Skowron, A. (2005) · Lecture notes in computer science · doi:10.1007/11590316_116 · Cited by 24
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Słowiński, K., Stefanowski, J., & Siwiński, D. (2002) · Fundamenta Informaticae · doi:10.3233/fun-2002-533-408 · Cited by 24
Algebraic Structures Related to Many Valued Logical Systems. Part I: Heyting Wajsberg Algebras
Cattaneo, G., Ciucci, D., Giuntini, R., et al. (2004) · Fundamenta Informaticae · doi:10.3233/fun-2004-63402 · Cited by 24
Negoita, M. G. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/846183.846212 · Cited by 24
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Peters, J. F., & Henry, C. J. (2006) · Fundamenta Informaticae · doi:10.3233/fun-2006-712-310 · Cited by 24
Grzymala‐Busse, J. W., Grzymała-Busse, W. J., & Goodwin, L. (2001) · Computational Intelligence · doi:10.1111/0824-7935.00155 · Cited by 23
Neuro-rough control of masking thresholds for audio signal enhancement
Czyżewski, A., & Królikowski, R. (2001) · Neurocomputing · doi:10.1016/s0925-2312(00)00333-7 · Cited by 23
Vakarelov, D. (2005) · Lecture notes in computer science · doi:10.1007/11548669_2 · Cited by 22
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Polkowski, L. (2003) · Fundamenta Informaticae · Cited by 22
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Munakata, T. (1997) · Springer eBooks · doi:10.1007/978-1-4613-1461-5_4 · Cited by 21
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Skowron, A., & Świniarski, R. W. (2005) · Lecture notes in computer science · doi:10.1007/11548669_4 · Cited by 21
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Grzymala‐Busse, J. W. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_13 · Cited by 21
Selected Algorithms of Machine Learning from Examples
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Peters, J. F., & Ramanna, S. (2003) · Software Quality Journal · doi:10.1023/a:1023764510838 · Cited by 19
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Jensen, R., Shen, Q., & Tuson, A. L. (2005) · Lecture notes in computer science · doi:10.1007/11548669_21 · Cited by 19
Flasiński, M., & Jurek, J. (2006) · Jagiellonian University Repository (Jagiellonian University) · Cited by 19
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Świniarski, R. W., & Skowron, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27794-1_19 · Cited by 18
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Pancerz, K., & Suraj, Z. (2004) · Fundamenta Informaticae · Cited by 18
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Inuiguchi, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25929-9_3 · Cited by 18
Tsumoto, S., & Ziarko, W. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61286-6_168 · Cited by 17
Approximate Boolean Reasoning Approach to Rough Sets and Data Mining
Nguyen, H. S. (2005) · Lecture notes in computer science · doi:10.1007/11548706_2 · Cited by 17
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Approximate Reasoning in Distributed Environments
Skowron, A. (2004) · Springer eBooks · doi:10.1007/978-3-662-07952-2_18 · Cited by 17
Self-citationRauszer, C. M. (1985) · Lecture notes in computer science · doi:10.1007/3-540-16066-3_25 · Cited by 17
Rasiowa, H., & Skowron, A. (1986) · Lecture notes in computer science · doi:10.1007/3-540-16444-8_8 · Cited by 17
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Quafafou, M., & Boussouf, M. (2000) · Intelligent Data Analysis · doi:10.3233/ida-2000-4102 · Cited by 16
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Muraszkiewicz, M., & Rybiński, H. (1994) · Workshops in computing · doi:10.1007/978-1-4471-3238-7_51 · Cited by 16
Banerjee, M. (2006) · Fundamenta Informaticae · doi:10.3233/fun-2006-712-301 · Cited by 16
Eliciting Domain Knowledge in Handwritten Digit Recognition
Nguyen, T. T. (2005) · Lecture notes in computer science · doi:10.1007/11590316_123 · Cited by 16
Polkowski, L. (1992) · Springer eBooks · doi:10.1007/978-94-015-7975-9_19 · Cited by 16
Rough Set Methodology in Clinical Practice: Controlled Hospital Trial of the MET System
Farion, K. J., Michalowski, W., Słowiński, R., et al. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25929-9_103 · Cited by 15
Processing of Musical Data Employing Rough Sets and Artificial Neural Networks
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Pindur, R., Susmaga, R., & Stefanowski, J. (2003) · Cited by 15
Rough Problem Settings for ILP Dealing With Imperfect Data
Liu, C., & Zhong, N. (2001) · Computational Intelligence · doi:10.1111/0824-7935.00157 · Cited by 15
A duality between Pawlak's knowledge representation systems and bi-consequence systems
Vakarelov, D. (1995) · Studia Logica · doi:10.1007/bf01053038 · Cited by 15
Concept Learning with Approximation: Rough Version Spaces
Dubois, V., & Quafafou, M. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45813-1_31 · Cited by 15
Knowledge Representation Systems for Groups of Agents
Rauszer, C. M. (1994) · Springer eBooks · doi:10.1007/978-94-015-8273-5_15 · Cited by 14
Cattaneo, G., Ciucci, D., Giuntini, R., et al. (2004) · Fundamenta Informaticae · doi:10.3233/fun-2004-63403 · Cited by 14
Skowron, A., & Synak, P. (2004) · Cited by 14
Self-citationProcessing of Musical Metadata Employing Pawlak’s Flow Graphs
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Pawlak, Z., Polkowski, L., & Skowron, A. (2000) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1840-6_13 · Cited by 13
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Marek, W., & Rasiowa, H. (1986) · Theoretical Computer Science · doi:10.1016/0304-3975(86)90092-7 · Cited by 13
Han, S., & Wang, J. (2004) · Journal of Computer Science and Technology · doi:10.1007/bf02944745 · Cited by 13
Towards a Line-Crawling Robot Obstacle Classification System: A Rough Set Approach
Peters, J. F., Ramanna, S., & Szczuka, M. S. (2007) · Springer eBooks · doi:10.1007/3-540-39205-x_44 · Cited by 12
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Świniarski, R. W. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45554-x_70 · Cited by 12
On Model Evaluation, Indexes of Importance, and Interaction Values in Rough Set Analysis
Gediga, G., & Düntsch, L. (2004) · Cognitive technologies · doi:10.1007/978-3-642-18859-6_10 · Cited by 12
The Paradox of the Heap of Grains in Respect to Roughness, Fuzziness and Negligibility
Marcus, S. (1998) · Lecture notes in computer science · doi:10.1007/3-540-69115-4_2 · Cited by 12
Approximation Space for Software Models
Peters, J. F., & Ramanna, S. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27794-1_16 · Cited by 12
Soft Computing-Based Recognition of Musical Sounds
Kostek, B. (1998) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1883-3_11 · Cited by 12
Rough Validity, Confidence, and Coverage of Rules in Approximation Spaces
Gomolińska, A. (2005) · Lecture notes in computer science · doi:10.1007/11427834_3 · Cited by 12
Data Mining Methods Supporting Diagnosis of Melanoma
Grzymala‐Busse, J. W., & HIPPE, Z. S. (2005) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2005.46 · Cited by 12
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Czyżewski, A., Szczerba, M., & Kostek, B. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27794-1_12 · Cited by 11
Rough Sets and Bayesian Methods Applied to Cancer Detection
Świniarski, R. W. (1998) · Lecture notes in computer science · doi:10.1007/3-540-69115-4_86 · Cited by 11
A Framework for Reasoning with Rough Sets
Vitória, A. (2005) · Lecture notes in computer science · doi:10.1007/11574798_10 · Cited by 11
On Fractal Dimension in Information Systems. Toward Exact Sets in Infinite Information Systems
Polkowski, L. (2002) · Fundamenta Informaticae · doi:10.3233/fun-2002-503-404 · Cited by 11
Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence)
Lin, T. Y., Ohsuga, S., Liau, C., et al. (2005) · Springer eBooks · Cited by 11
Rough sets: probabilistic versus deterministic approach
Pawlak, Z., Wong, S. K. M., & Ziaro, W. (1990) · Our Digital Library (Warsaw University of Technology) · Cited by 11
Self-citationA Graded Meaning of Formulas in Approximation Spaces
Gomolińska, A. (2004) · Fundamenta Informaticae · Cited by 10
Automated discovery of chronological patterns in long time-series medical datasets
Tsumoto, S., & Hirano, S. (2005) · International Journal of Intelligent Systems · doi:10.1002/int.20093 · Cited by 10
Axiomatization and completeness of uncountably valued approximation logic
Rasiowa, H. (1994) · Studia Logica · doi:10.1007/bf01053027 · Cited by 10
Formalization and Induction of Medical Expert System Rules Based on Rough Set Theory
Tsumoto, S. (1998) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1883-3_16 · Cited by 10
Approximation Transducers and Trees: A Technique for Combining Rough and Crisp Knowledge
Doherty, P., Łukaszewicz, W., Skowron, A., et al. (2004) · Cognitive technologies · doi:10.1007/978-3-642-18859-6_8 · Cited by 9
Self-citationRough Neurocomputing: A Survey of Basic Models of Neurocomputation
Peters, J. F., & Szczuka, M. S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45813-1_40 · Cited by 9
Mathematical Methods of Specification and Synthesis of Software Systems ‘85
(1986) · De Gruyter eBooks · doi:10.1515/9783112733417 · Cited by 9
Rough Set Approximations of Languages
un, G. P. C., Polkowski, L., & Skowron, A. (1997) · Fundamenta Informaticae · doi:10.3233/fi-1997-32204 · Cited by 8
Self-citationOn a Logic of Information for Reasoning about Knowledge
Nakamura, A. (1994) · Workshops in computing · doi:10.1007/978-1-4471-3238-7_23 · Cited by 8
On Modal and Fuzzy Decision Logics Based on Rough Set Theory
Fan, T.-F., Liau, C., & Yao, Y. Y. (2002) · Fundamenta Informaticae · doi:10.3233/fun-2002-52403 · Cited by 8
Meningitis data mining by cooperatively using GDT-RS and RSBR
Zhong, N., Dong, J.-Z., & Ohsuga, S. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00200-3 · Cited by 8
Supervised Learning in the Gene Ontology Part II: A Bottom-Up Algorithm
Midelfart, H. (2005) · Lecture notes in computer science · doi:10.1007/11574798_6 · Cited by 8
Logic for Information Systems1
Rauszer, C. M. (1992) · Fundamenta Informaticae · doi:10.3233/fi-1992-163-410 · Cited by 7
Generalized Rough Sets in Contextual Spaces
Bryniarski, E., & Wybraniec-Skardowska, U. (1997) · Springer eBooks · doi:10.1007/978-1-4613-1461-5_17 · Cited by 7
Classification of Unseen Examples under Uncertainty
Grzymala‐Busse, J. W. (1997) · Fundamenta Informaticae · doi:10.3233/fi-1997-303403 · Cited by 7
A New Halftoning Method Based on Error Diffusion with Rough Set Filtering
Zeng, H., & Świniarski, R. W. (1998) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1883-3_18 · Cited by 7
Data mining tasks and methods: Classification: multicriteria classification
Greco, S., Matarazzo, B., & Słowiński, R. (2002) · Oxford University Press eBooks · Cited by 6
Approximate Markov Boundaries and Bayesian Networks: Rough Set Approach
Ślȩzak, D. (2003) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-36473-3_11 · Cited by 6
Automata-Theoretic Decision Procedures for Information Logics
Demri, S. P., & Sattler, U. (2002) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.5555/1220762.1220763 · Cited by 6
Toward Rough Datalog: Embedding Rough Sets in Prolog
Małuszyński, J., & Vitória, A. (2004) · Cognitive technologies · doi:10.1007/978-3-642-18859-6_12 · Cited by 6
Characterizations of Attributes in Generalized Approximation Representation Spaces
Qiu, G.-F., Zhang, W., & Wu, W.-Z. (2005) · Lecture notes in computer science · doi:10.1007/11548669_9 · Cited by 6
Interval Regression Analysis with Polynomials and Its Similarity to Rough Sets Concept
Tanaka, H., & Lee, H. (1999) · Fundamenta Informaticae · doi:10.3233/fi-1999-371204 · Cited by 6
Modular Rough Fuzzy MLP: Evolutionary Design
Mitra, P., Mitra, S., & Pal, S. K. (1999) · Lecture notes in computer science · doi:10.1007/978-3-540-48061-7_17 · Cited by 6
Brown, I., & Ward, H. W. C. (1977) · Cancer Letters · doi:10.1016/s0304-3835(77)80026-8 · Cited by 6
Reducts-driven query answering for distributed autonomous knowledge systems
Ra, Z. W. (2002) · International Journal of Intelligent Systems · doi:10.1002/int.10011 · Cited by 6
Processing of Musical Data Employing Rough Sets and Artificial Neural Networks
Kostek, B., Szczuko, P., Żwan, P., et al. (2005) · Lecture notes in computer science · doi:10.1007/11427834_5 · Cited by 6
Bazan, J. G., Nguyen, H. S., Peters, J. F., et al. (2003) · Electronic Notes in Theoretical Computer Science · Cited by 5
Data Mining as Generalization: A Formal Model.
Menasalvas, E., & Wasilewska, A. (2006) · Cited by 4
Valdés, J. J., & Barton, A. J. (2005) · Lecture notes in computer science · doi:10.1007/11548706_38 · Cited by 4
Transactions on Rough Sets II: Rough Sets and Fuzzy Sets (Lecture Notes in Computer Science)
Peters, J. F., Skowron, A., Dubois, D., et al. (2005) · Springer eBooks · Cited by 4
Self-citationCollaborative Query Processing in DKS Controlled by Reducts
Raś, Z. W., & Dardzińska, A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45813-1_24 · Cited by 3
Using Rough Set Theory for Detecting the Interaction Terms in a Generalized Logit Model
Ong, C., Huang, J., & Tzeng, G. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25929-9_77 · Cited by 3
Automata-Theoretic Decision Procedures for Information Logics
Demri, S. P., & Sattler, U. (2002) · Fundamenta Informaticae · doi:10.3233/fun-2002-53101 · Cited by 3
On Fractal Dimension in Information Systems
Polkowski, L. (2003) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-36473-3_8 · Cited by 2
Computational Complexity of Multimodal Logics Based on Rough Sets
Demri, S. P., & Stepaniuk, J. (2000) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 2
On Partial Tests and Partial Reducts for Decision Tables
Moshkov, M., & Piliszczuk, M. (2005) · Lecture notes in computer science · doi:10.1007/11548669_16 · Cited by 2
Computational Complexity of Multimodal Logics \newline Based on Rough Sets
Demri, S. P., & Stepaniuk, J. (2000) · Fundamenta Informaticae · doi:10.3233/fun-2000-44403 · Cited by 2
Dual Mathematical Models Based on Rough Approximations in Data Analysis
Tanaka, H. (2007) · Springer eBooks · doi:10.1007/3-540-39205-x_7 · Cited by 1
Research works printed but not linked: 64
- 2002, vol. 2475
- P. Apostoli, A. Kanda, Parts of the continuum: Towards a modern ontology of sciences, Technical Reports in Philosophical Logic, vol. 96 (1). The University of Toronto, Department of Philosophy, Toronto, Canada, 1999, Revised March, 1999.
- M. Banerjee, M.K. Chakraborty, Rough set algebras. In: Pal et al. [194], pp. 157–184.
- J.G. Bazan, A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables. In: Polkowski and Skowron [244], pp. 321–365.
- G. Cattaneo, Abstract approximation spaces for rough theories. In: Polkowski and Skowron [244], pp. 59–98.
- 2001, Computational Intelligence: An International Journal, vol. 17
- Doherty, 2006, vol. 202
- Forrest, 2002, Sets as mereological tropes, Metaphysical, 3, 5
- 1994, vol. 3
- Hu, 1995, A rough set approach to compute all maximal generalized rules, Journal of Computing and Information, 1, 1078
- Hu, 2004, A new rough set model based on database systems, Journal of Fundamental Informatics, 59, 135
- 2003, vol. 125
- T. Iwiński, Rough analysis of lattices, Working papers, vol. 23. University of Carlos III, Madrid, 1991.
- J. Järvinen, Representation of information systems and dependence spaces, and some basic algorithms. Licentiate’s thesis. Ph.D. thesis, University of Turku, Department of Mathematics, Turku, Finland, 1997.
- J. Komorowski, Z. Pawlak, L. Polkowski, A. Skowron, Rough sets: a tutorial. In: Pal and Skowron [195], pp. 3–98.
- Kostek, 1999, vol. 31
- Kostek, 2005, vol. 3
- M. Kryszkiewicz, Properties of incomplete information systems in the framework of rough sets. In: Polkowski and Skowron [244], pp. 422–450.
- A.O.V. Le Blanc, Lesniewski’s Computative Protothetic. Report (Ph.D. thesis), University of Manchester, Manchester, UK, 2003.
- 1996, Journal of the Intelligent Automation and Soft Computing, vol. 2
- J. Małuszyński, A. Vitória, Toward rough datalog. In: Pal et al. [194], pp. 297–332.
- S.H. Nguyen, H.S. Nguyen, Some efficient algorithms for rough set methods. In: Sixth International Conference on Information Processing and Management of Uncertainty on Knowledge Based Systems IPMU’1996, Granada, Spain, 1996, vol. III, pp. 1451–1456.
- Novotný, 1991, Algebraic theory of independence in information systems, Fundamenta Informaticae, 14, 454, 10.3233/FI-1991-14404 · DOI
- E. Orłowska, Rough concept logic. In: Skowron [272], pp. 177–186.
- Orłowska, 1987, Reasoning about vague concepts, Bulletin of the Polish Academy of Sciences, Mathematics, 35, 643
- 1997, vol. 13
- Pagliani, 2004, Pretopologies and dynamic spaces, Fundamenta Informaticae, 59, 221
- 2005, vol. 3776
- 2001, Rough-neuro computing, Neurocomputing, 36
- Z. Pawlak, Rough Relations, Reports, vol. 435. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, 1981.
- Pawlak, 1987, Rough logic, Bulletin of the Polish Academy of Sciences, Technical Sciences, 35, 253
- Pawlak, 1991, Decision logic, Bulletin of the EATCS, 44, 201
- Pawlak, 1991, vol. 9
- 2001, International Journal of Intelligent Systems, vol. 16
- 2004, vol. 3100
- 2005, vol. 3400
- 2005, vol. 3700
- 2004, vol. 3135
- 2000, vol. 56
- 1998, vol. 1424
- 1998, vol. 18
- 1998, vol. 19
- Pomykała, 1988, The stone algebra of rough sets, Bulletin of the Polish Academy of Sciences, Mathematics, 36, 495
- Rauszer, 1985, An equivalence between theory of functional dependence and a fragment of intuitionistic logic, Bulletin of the Polish Academy of Sciences, Mathematics, 33, 571
- 1985, vol. 208
- Skowron, 2000, Rough sets in KDD – plenary talk, 1
- 2003, Pattern Recognition Letters, vol. 24
- 2003, vol. 82(4)
- D. Śle¸zak, Approximate reducts in decision tables. In: Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU’1996. Granada, Spain, 1996, vol. III, pp. 1159–1164.
- 2005, vol. 3641
- 2005, vol. 3642
- K. Słowiński, J. Stefanowski, Medical information systems – problems with analysis and way of solution. In: Pal and Skowron [195], pp. 301–315.
- 1992, vol. 11
- R. Słowiński, J. Stefanowski (Eds.). Special issue: Proceedings of the First International Workshop on Rough Sets: State of the Art and Perspectives, Kiekrz, Poznań, Poland, September 2–4 (1992). In: Foundations of Computing and Decision Sciences, vol. 18(3–4). 1993.
- Słowiński, 2000, Rough sets processing of inconsistent information, Control and Cybernetics, 29, 379
- R. Swiniarski, Rough sets and principal component analysis and their applications. data model building and classification. In: Pal and Skowron [195], pp. 275–300.
- 2001, vol. 2253
- 2004, vol. 3066
- 2003, vol. 2639
- A. Wasilewska, L. Vigneron, Rough algebras and automated deduction. In: Polkowski and Skowron [244], pp. 261–275.
- Y.Y. Yao, Generalized rough set models. In: Polkowski and Skowron [244], pp. 286–318.
- 1995, An International Journal, 11
- 1996, Fundamenta Informaticae, 27
- 2001, vol. 2005
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AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103599 · Cited by 671
“Some systems integrate basic learning capabilities [72] through feed-back loops [73,74], heuristics [75], or updated context buffers to refine behavior over time, particularly in settings like personalized recommendations or conversation flow management [76– 78].”
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15161 · Cited by 186
Metadata
- Authors
- Pawlak, Z., & Skowron, A.
- Affiliations
- University of Warsaw (PL)
- Corresponding author
- Skowron, Andrzej
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 177
- Issue
- 1
- Pages
- 3-27
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2143451122
- Cited by
- 2,070
- References
- 352
- Citations by year
- 2026: 28 · 2025: 49 · 2024: 61 · 2023: 65 · 2022: 67 · 2021: 89 · 2020: 92 · 2019: 102 · 2018: 108 · 2017: 85 · 2016: 120 · 2015: 125 · 2014: 139 · 2013: 129 · 2012: 182
- FWCI
- 111.73
- Type
- journal article
- Language
- en
- Open access
- green
- bcpw.bg.pw.edu.pl
- Locations
- Information Sciences (publishedVersion)
- Our Digital Library (Warsaw University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Rough Sets and Fuzzy Logic
- Social Sciences › Decision Sciences › Management Science and Operations Research › Fuzzy and Soft Set Theory
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Algebra and Logic
- Keywords
- rough set theory, artificial intelligence, knowledge discovery, decision analysis, expert systems, pattern recognition, inductive reasoning, mereology
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
