On rough sets, their recent extensions and applications

N.Mac Parthalain and Q. Shen. On rough sets, their recent extensions, and applications, The Knowledge Engineering Review, volume 25, issue 04, pp. 365-395, 2010.

[1]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[2]  Tsau Young Lin,et al.  Feature Subset Selection Based on Relative Dependency between Attributes , 2004, Rough Sets and Current Trends in Computing.

[3]  Z. Pawlak,et al.  A Rough Set Perspective on Data andKnowledge ? , 1999 .

[4]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[5]  Tsau Young Lin,et al.  Rough Sets and Data Mining: Analysis of Imprecise Data , 1996 .

[6]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[7]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[8]  Vijay V. Raghavan,et al.  Exploiting Upper Approximation in the Rough Set Methodology , 1995, KDD.

[9]  Daijin Kim,et al.  A Handwritten Numeral Character Classification Using Tolerant Rough Set , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Andrzej Skowron,et al.  Searching for Relational Patterns in Data , 1997, PKDD.

[11]  Malcolm J. Beynon,et al.  Reducts within the variable precision rough sets model: A further investigation , 2001, Eur. J. Oper. Res..

[12]  Jianwen Ma,et al.  Land cover classification based on tolerant rough set , 2006 .

[13]  Xiangyang Wang,et al.  Feature selection based on rough sets and particle swarm optimization , 2007, Pattern Recognit. Lett..

[14]  Chris Cornelis,et al.  A New Approach to Fuzzy-Rough Nearest Neighbour Classification , 2008, RSCTC.

[15]  Andrzej Skowron,et al.  Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems , 1998 .

[16]  Alfredo Petrosino,et al.  Unsupervised texture discrimination based on rough fuzzy sets and parallel hierarchical clustering , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[17]  Manish Sarkar,et al.  Fuzzy-rough nearest neighbors algorithm , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[18]  Mircea Gh. Negoita,et al.  Real World Applications of Computational Intelligence (Studies in Fuzziness and Soft Computing) , 2005 .

[19]  Ivo Düntsch,et al.  IRIS Revisited: A Comparison of Discriminant and Enhanced Rough Set Data Analysis , 1998 .

[20]  Ming-Wen Shao,et al.  Dominance relation and rules in an incomplete ordered information system , 2005 .

[21]  Xinyu Shao,et al.  Integration of Variable Precision Rough Set and Fuzzy Clustering: An Application to Knowledge Acquisition for Manufacturing Process Planning , 2005, RSFDGrC.

[22]  Lluís A. Belanche Muñoz,et al.  Feature selection algorithms: a survey and experimental evaluation , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[23]  Jerzy Stefanowski,et al.  Application of Rule Induction and Rough Sets to Verification of Magnetic Resonance Diagnosis , 2002, Fundam. Informaticae.

[24]  Qiang Shen,et al.  New Approaches to Fuzzy-Rough Feature Selection , 2009, IEEE Transactions on Fuzzy Systems.

[25]  Zuren Feng,et al.  An efficient ant colony optimization approach to attribute reduction in rough set theory , 2008, Pattern Recognit. Lett..

[26]  Jan G. Bazan,et al.  Rough set algorithms in classification problem , 2000 .

[27]  M. Narasimha Murty,et al.  An adaptive rough fuzzy single pass algorithm for clustering large data sets , 2003, Pattern Recognit..

[28]  Renpu Li,et al.  Mining classification rules using rough sets and neural networks , 2004, Eur. J. Oper. Res..

[29]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[30]  Daniel Vanderpooten,et al.  A Generalized Definition of Rough Approximations Based on Similarity , 2000, IEEE Trans. Knowl. Data Eng..

[31]  Zheng Pei,et al.  On the topological properties of fuzzy rough sets , 2005, Fuzzy Sets Syst..

[32]  Roman W. Swiniarski,et al.  Rough Sets and Bayesian Methods Applied to Cancer Detection , 1998, Rough Sets and Current Trends in Computing.

[33]  J. Grzymala-Busse.  A Comparison of Three Strategies to Rule Induction from Data with Numerical Attributes , 2003, Electron. Notes Theor. Comput. Sci..

[34]  Sankar K. Pal,et al.  Staging of cervical cancer with soft computing , 2000, IEEE Transactions on Biomedical Engineering.

[35]  Qiang Shen,et al.  Fuzzy-rough data reduction with ant colony optimization , 2005, Fuzzy Sets Syst..

[36]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[37]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[38]  C. Cornelis,et al.  Vaguely Quantified Rough Sets , 2009, RSFDGrC.

[39]  Qing-Hua Hu,et al.  Variable precision dominance based rough set model and reduction algorithm for preference-ordered data , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[40]  Chris Cornelis,et al.  A noise-tolerant approach to fuzzy-rough feature selection , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[41]  B.F. Momin,et al.  Reduct Generation and Classification of Gene Expression Data , 2006, 2006 International Conference on Hybrid Information Technology.

[42]  Wojciech Ziarko,et al.  Rough Set Approach to Spam Filter Learning , 2007, RSEISP.

[43]  Xizhao Wang,et al.  On the generalization of fuzzy rough sets , 2005, IEEE Transactions on Fuzzy Systems.

[44]  Chris Cornelis,et al.  Fuzzy rough sets: beyond the obvious , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[45]  Anna Maria Radzikowska,et al.  Fuzzy Rough Sets Based on Residuated Lattices , 2004, Trans. Rough Sets.

[46]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[47]  Maciej Wygralak.  Rough sets and fuzzy sets—some remarks on interrelations , 1989 .

[48]  V. Deved,et al.  Proceedings of the 24th IASTED international conference on Artificial intelligence and applications , 2006 .

[49]  Shusaku Tsumoto,et al.  Indiscernibility-Based Clustering: Rough Clustering , 2003, IFSA.

[50]  Yaqin Zhao,et al.  A Rough Set-Based Fuzzy Clustering , 2005, AIRS.

[51]  DecisionTablesJitender S. DeogunThe,et al.  Rough Set Based Classiication Methods and Extended Decision Tables , 1994 .

[52]  H. Bian,et al.  Fuzzy-rough nearest-neighbor classification approach , 2003, 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003.

[53]  Aleksander Øhrn,et al.  Discernibility and Rough Sets in Medicine: Tools and Applications , 2000 .

[54]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[55]  Nehad N. Morsi,et al.  Axiomatics for fuzzy rough sets , 1998, Fuzzy Sets Syst..

[56]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[57]  Dominik Slezak,et al.  Approximate Reducts and Association Rules - Correspondence and Complexity Results , 1999, RSFDGrC.

[58]  Xu Qing-yuan.  Generalized Approximation spaces Based on Inverse Serial Relation , 2004 .

[59]  Yee Leung,et al.  On characterizations of (I, J)-fuzzy rough approximation operators , 2005, Fuzzy Sets Syst..

[60]  Andrzej Skowron,et al.  A rough set approach to decision rules generation , 1993 .

[61]  Zi-Ru Zhang,et al.  A generalized definition of rough approximation based on similarity in variable precision rough sets , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[62]  Qiang Shen,et al.  A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems , 2000 .

[63]  Qiang Shen,et al.  Rough and Fuzzy-rough methods for mammographic data analysis , 2008 .

[64]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[65]  Roman Slowinski,et al.  Rough Classification of Patients After Highly Selective Vagotomy for Duodenal Ulcer , 1986, Int. J. Man Mach. Stud..

[66]  Rajen B. Bhatt,et al.  FRID: fuzzy-rough interactive dichotomizers , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[67]  Arunabha Bagchi,et al.  Filtering and identification of stochastic volatility for parabolic type factor models , 2006 .

[68]  Qiang Shen,et al.  Exploring the boundary region of tolerance rough sets for feature selection , 2009, Pattern Recognit..

[69]  Wen-Xiu Zhang,et al.  An axiomatic characterization of a fuzzy generalization of rough sets , 2004, Inf. Sci..

[70]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[71]  Qinghua Hu,et al.  Information-preserving hybrid data reduction based on fuzzy-rough techniques , 2006, Pattern Recognit. Lett..

[72]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[73]  Qiang Shen,et al.  A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction , 2010, IEEE Transactions on Knowledge and Data Engineering.

[74]  Sankar K. Pal,et al.  Pattern Recognition Algorithms for Data Mining , 2004 .

[75]  Qiang Shen,et al.  Rough set-aided keyword reduction for text categorization , 2001, Appl. Artif. Intell..

[76]  Wang Ju,et al.  Reduction algorithms based on discernibility matrix: The ordered attributes method , 2001, Journal of Computer Science and Technology.

[77]  Maciej Modrzejewski,et al.  Feature Selection Using Rough Sets Theory , 1993, ECML.

[78]  Qinghua Hu,et al.  Consistency Based Attribute Reduction , 2007, PAKDD.

[79]  Yiyu Yao.  Combination of Rough and Fuzzy Sets Based on α-Level Sets , 1997 .

[80]  David A. Bell,et al.  Computational Methods for Rough Classification and Discovery , 1998, J. Am. Soc. Inf. Sci..

[81]  Naomie Salim,et al.  Independent Component Analysis and Rough Fuzzy based Approach to Web Usage Mining , 2006, Artificial Intelligence and Applications.

[82]  Pradeep Kumar,et al.  Rough clustering of sequential data , 2007, Data Knowl. Eng..

[83]  Tu Bao Ho,et al.  Hierarchical Document Clustering Based on Tolerance Rough Set Model , 2000, PKDD.

[84]  Pawan Lingras,et al.  Comparison of Neofuzzy and Rough Neural Networks , 1998, Inf. Sci..

[85]  S. Srinoy,et al.  Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering , 2006, 2006 International Conference on Hybrid Information Technology.

[86]  Wei-Zhi Wu,et al.  Generalized fuzzy rough sets , 2003, Inf. Sci..

[87]  Jerzy W. Grzymala-Busse,et al.  THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION , 1995, Comput. Intell..

[88]  Qiang Shen,et al.  Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches , 2008, IEEE Press series on computational intelligence.

[89]  Donald H. Kraft,et al.  Vocabulary mining for information retrieval: rough sets and fuzzy sets , 2001, Inf. Process. Manag..

[90]  Mingyang Li,et al.  An Extension of VPRS Model Based on Dominance Relation , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[91]  Zdzislaw Pawlak,et al.  Some Issues on Rough Sets , 2004, Trans. Rough Sets.

[92]  Qiang Shen,et al.  Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring , 2004, Pattern Recognit..

[93]  Toshinori Munakata,et al.  Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3 , 2002, Eur. J. Oper. Res..

[94]  Andrzej Skowron,et al.  Boolean Reasoning for Decision Rules Generation , 1993, ISMIS.

[95]  Reyer Zwiggelaar,et al.  Fuzzy-rough approaches for mammographic risk analysis , 2010, Intell. Data Anal..

[96]  Tzung-Pei Hong,et al.  Learning with Hierarchical Quantitative Attributes by Fuzzy Rough Sets , 2006, JCIS.

[97]  Manish Sarkar,et al.  Fuzzy-rough nearest neighbor algorithms in classification , 2007, Fuzzy Sets Syst..

[98]  Qiang Shen,et al.  Fuzzy-Rough Sets Assisted Attribute Selection , 2007, IEEE Transactions on Fuzzy Systems.

[99]  Jonathan Lawry,et al.  A mass assignment based ID3 algorithm for decision tree induction , 1997, Int. J. Intell. Syst..

[100]  Pawan Lingras,et al.  Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets , 2004, Web Intell. Agent Syst..

[101]  Xizhao Wang,et al.  Attributes Reduction Using Fuzzy Rough Sets , 2008, IEEE Transactions on Fuzzy Systems.

[102]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[103]  Tu Bao Ho,et al.  Documents Clustering Using Tolerance Rough Set Model and Its Application to Information Retrieval , 2003, Intelligent Exploration of the Web.

[104]  Andrzej Skowron,et al.  Tolerance Approximation Spaces , 1996, Fundam. Informaticae.

[105]  Qiang Shen,et al.  Centre for Intelligent Systems and Their Applications Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Sets and Systems ( ) – Fuzzy–rough Attribute Reduction with Application to Web Categorization , 2022 .

[106]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[107]  Pawan Lingras,et al.  Interval Set Clustering of Web Users with Rough K-Means , 2004, Journal of Intelligent Information Systems.

[108]  Zhengding Lu,et al.  Web Document Classification Based on Extended Rough set , 2005, Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05).

[109]  Zhou Xianzhong,et al.  A rough set-based fuzzy clustering , 2005 .

[110]  Piotr Wojdyllo,et al.  Wavelets, Rough Sets and Artificial Neural Networks in EEG Analysis , 1998, Rough Sets and Current Trends in Computing.

[111]  Qiang Shen,et al.  Rough Feature Selection for Neural Network Based Image Classification , 2002, Int. J. Image Graph..

[112]  Qinghua Hu,et al.  Fuzzy rough c-means clustering for image colour quantisation , 2009 .

[113]  Tianxu Zhang,et al.  A Hybrid Method for Relevance Feedback in Image Retrieval Using Rough Sets and Neural Networks , 2005 .

[114]  D. Vanderpooten.  Similarity Relation as a Basis for Rough Approximations , 1995 .

[115]  Zdzislaw Pawlak,et al.  Rough classification , 1984, Int. J. Hum. Comput. Stud..

[116]  Thomas E. McKee,et al.  Genetic programming and rough sets: A hybrid approach to bankruptcy classification , 2002, Eur. J. Oper. Res..

[117]  Xiaoxia Zheng,et al.  Power transformer fault diagnosis based on variable precision rough set , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[118]  Wojciech Ziarko,et al.  Acquisition of hierarchy‐structured probabilistic decision tables and rules from data , 2003, Expert Syst. J. Knowl. Eng..

[119]  Wei-Zhi Wu,et al.  Constructive and axiomatic approaches of fuzzy approximation operators , 2004, Inf. Sci..

[120]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[121]  Malcolm J. Beynon.  An Investigation of beta-Reduct Selection within the Variable Precision Rough Sets Model , 2000, Rough Sets and Current Trends in Computing.

[122]  Didier Dubois,et al.  Putting Rough Sets and Fuzzy Sets Together , 1992, Intelligent Decision Support.

[123]  Qiang Shen,et al.  A rough-fuzzy approach for generating classification rules , 2002, Pattern Recognit..

[124]  Quan Pan,et al.  Classification Using the Variable Precision Rough Set , 2003, RSFDGrC.

[125]  Xiangyang Wang,et al.  Fuzzy-Rough Set Based Nearest Neighbor Clustering Classification Algorithm , 2005, FSKD.

[126]  Jaroslaw Stepaniuk,et al.  Standard and Fuzzy Rough Entropy Clustering Algorithms in Image Segmentation , 2008, RSCTC.

[127]  Wojciech Kotlowski,et al.  Stochastic dominance-based rough set model for ordinal classification , 2008, Inf. Sci..

[128]  Daniel S. Yeung,et al.  Rough approximations on a complete completely distributive lattice with applications to generalized rough sets , 2006, Inf. Sci..

[129]  Jan M. Zytkow,et al.  Handbook of Data Mining and Knowledge Discovery , 2002 .

[130]  A. Skowron,et al.  Clustering : A Rough Set Approach to Constructing Information Granules , 2002 .

[131]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[132]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[133]  Shusaku Tsumoto,et al.  Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic , 2000, Inf. Sci..

[134]  Bing Han,et al.  A Novel Clustering Algorithm Based on Variable Precision Rough-Fuzzy Sets , 2006, ICIC.

[135]  Chengdong Wu,et al.  An Improved BP Network Classifier Based on VPRS Feature Reduction , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[136]  Yongli Zhu,et al.  Classifying Email Using Variable Precision Rough Set Approach , 2006, RSKT.

[137]  Anna Maria Radzikowska,et al.  A comparative study of fuzzy rough sets , 2002, Fuzzy Sets Syst..

[138]  Tu Bao Ho,et al.  Nonhierarchical document clustering based on a tolerance rough set model , 2002, Int. J. Intell. Syst..

[139]  P. Lingras.  Rough Neural Networks , 1996 .

[140]  Pawan Lingras,et al.  Applications of Rough Genetic Algorithms , 2001, Comput. Intell..

[141]  Marzena Kryszkiewicz.  Maintenance of Reducts in the Variable Precision Rough Set Model , 1997 .

[142]  Wei-Zhi Wu,et al.  A Study on Relationship Between Fuzzy Rough Approximation Operators and Fuzzy Topological Spaces , 2005, FSKD.

[143]  Nick Cercone,et al.  Integrating rough set theory and medical applications , 2008, Appl. Math. Lett..

[144]  Qiang Shen,et al.  Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches , 2004, IEEE Transactions on Knowledge and Data Engineering.

[145]  Tsau Young Lin,et al.  Rough Set Methods and Applications , 2000 .

[146]  Hung Son Nguyen,et al.  A Tolerance Rough Set Approach to Clustering Web Search Results , 2004, PKDD.

[147]  J. Recasens,et al.  UPPER AND LOWER APPROXIMATIONS OF FUZZY SETS , 2000 .

[148]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[149]  Ning Zhong,et al.  Using Rough Sets with Heuristics for Feature Selection , 1999, RSFDGrC.

[150]  Jerzy Stefanowski,et al.  On rough set based approaches to induction of decision rules , 1998 .

[151]  Naohiro Ishii,et al.  Feature Weights Determining of Pattern Classification by Using a Rough Genetic Algorithm with Fuzzy Similarity Measure , 2002, IDEAL.

[152]  Qinghua Hu,et al.  Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation , 2007, Pattern Recognit..

[153]  H. Thiele.  Fuzzy Rough Sets versus Rough Fuzzy Sets — An Interpretation and a Comparative Study using Concepts of Modal Logics , 1998 .

[154]  Robert Marti,et al.  A Novel Breast Tissue Density Classification Methodology , 2008, IEEE Transactions on Information Technology in Biomedicine.

[155]  Donald W. Loveland,et al.  A machine program for theorem-proving , 2011, CACM.

[156]  Gilles Brassard,et al.  Fundamentals of algorithms , 1996 .

[157]  Alicja Mieszkowicz-Rolka,et al.  Fuzzy Implication Operators in Variable Precision Fuzzy Rough Sets Model , 2004, ICAISC.

[158]  Jerzy W. Grzymala-Busse,et al.  Rough Set Theory with Applications to Data Mining , 2011 .

[159]  Ramlan Mahmod,et al.  Rough neural expert systems , 2000 .