Topic
Rough set
About: Rough set is a research topic. Over the lifetime, 14683 publications have been published within this topic receiving 256804 citations.
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01 Aug 1992
TL;DR: The use of 'Rough Sets' Methods to draw Premonitory Factors for Earthquakes by emphasising Gas Geochemistry: The Case of a Low Seismic Activity Context in Belgium J.T. Polkowski is used.
Abstract: Preface Z. Pawlak. Scope and Goals of the Book R. Slowinski. Part I: Applications of the Rough Sets Approach to Intelligent Decision Support. 1. LERS -- A System for Learning from Examples Based on Rough Sets J.W. Grzymala-Busse. 2. Rough Sets in Computer Implemetation of Rule-Based Control of Industrial Process A. Mrozek. 3. Analysis of Diagnostic Symptoms in Vibroacoustic Diagnostic by Means of the Rough Sets Theory R. Nowicki, R. Slowinski, J. Stefanowski. 4. Knowledge-Based Process Control Using Rough Sets A.J. Szladown, W.P. Ziarko. 5. Acquisition of Control Algorithms from Operation Data W.P. Ziarko. 6. Rough Classification of HSV Patients K. Slowinski. 7. Surgical Wound Infection -- Conducive Factors and their Mutual Dependencies M. Kandulski, J. Marciniec, K. Tukallo. 8. Fuzzy Inference System Based on Rough Sets and its Application to Medical Diagnosis H. Tanaka, H. Ishibuchi, T. Shigenaga. 9. Analysis of Structure-Activity Relationships of Quaternary Ammonium Compounds J. Krysinski. 10. Rough Sets-Based Study of Voter Preference in 1988 U.S.A. Presidential Election M. Hadjimichale, A. Wasilewska. 11. An Application of Rough Set Theory in the Control of Water Conditions in a Polder A. Reinhard, B. Stawski, T. Weber, U. Wybraniec-Skardowska. 12. Use of 'Rough Sets' Methods to draw Premonitory Factors for Earthquakes by emphasising Gas Geochemistry: The Case of a Low Seismic Activity Context in Belgium J. Teghem, J.-M. Charlet. 13. Rough Sets and Some Aspects of Logic Synthesis T. Luba,J. Rybnik. Part II: Comparison with Related Methodologies. 1. Putting Rough Sets and Fuzzy Sets together D. Dubois, H. Prade. 2. Applications of Fuzzy-Rough Classification to Logics A. Nakamura. 3. Comparison of the Rough Sets Approach and Probalistic Data Analysis Techniques on a Common Set of Medical Data E. Krusinska, A. Babic, R. Slowinski, J. Stefanowski. 4. Some Experiments to Compare Rough Sets Theory and Ordinal Statistical Methods J. Teghem, M. Benjelloun. 5. Topological and Fuzzy Rough Sets T. Lin. 6. On Convergence of Rough Sets L.T. Polkowski. Part III: Further Developments. 1. Maintenance of Knowledge in Dynamic Systems M.E. Orlowska, M.W. Orlowski. 2. The Discernibility Matrices and Functions in Information Systems A. Skowron, C. Rauszer. 3. Sensitivity of Rough Classification to Changes in Norms of Attributes K. Slowinski, R. Slowinksi. 4. Discretization of Condition Attributes Space A. Lenarcik, Z. Piasta. 5. Consequence Relations and Information Systems D. Vakarelov. 6. Rough Grammar for High Performance Management of Processes on a Distributed System Z.M. Wojcik, B.E. Wojcik. 7. Learning Classification Rules from Database in the Context of Knowledge-Acquisition and Representation R. Yasdi. 8. 'RoughDAS' and 'RoughClass' Software Implementations of the Rough Sets Approach R. Slowinski, J. Stefanowski. Appendix: Glossary of Basic Concepts. Subject Index.
875 citations
TL;DR: The algorithm for feature selection is based on an application of a rough set method to the result of principal components analysis (PCA) used for feature projection and reduction.
801 citations
TL;DR: A new feature selection strategy based on rough sets and particle swarm optimization (PSO), which does not need complex operators such as crossover and mutation, and requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime.
794 citations
TL;DR: A neighborhood rough set model is introduced to deal with the problem of heterogeneous feature subset selection and Experimental results show that the neighborhood model based method is more flexible to deals with heterogeneous data.
780 citations