Parametric Matroid of Rough Set

Rough set is mainly concerned with the approximations of objects through an equivalence relation on a universe. Matroid is a generalization of linear algebra and graph theory. Recently, a matroidal structure of rough sets is established and applied to the problem of attribute reduction which is an important application of rough set theory. In this paper, we propose a new matroidal structure of rough sets and call it a parametric matroid. On the one hand, for an equivalence relation on a universe, a parametric set family, with any subset of the universe as its parameter, is defined through the lower approximation operator. This parametric set family is proved to satisfy the independent set axiom of matroids, therefore a matroid is generated, and we call it a parametric matroid of the rough set. Through the lower approximation operator, three equivalent representations of the parametric set family are obtained. Moreover, the parametric matroid of the rough set is proved to be the direct sum of a partition-circuit matroid and a free matroid. On the other hand, partition-circuit matroids are well studied through the lower approximation number, and then we use it to investigate the parametric matroid of the rough set. Several characteristics of the parametric matroid of the rough set, such as independent sets, bases, circuits, the rank function and the closure operator, are expressed by the lower approximation number.

[1]  Kun She,et al.  A matroidal approach to rough set theory , 2013, Theor. Comput. Sci..

[2]  Duoqian Miao,et al.  A rough set approach to feature selection based on ant colony optimization , 2010, Pattern Recognit. Lett..

[3]  William Zhu,et al.  Matroidal structure of covering-based rough sets through the upper approximation number , 2011, Int. J. Granul. Comput. Rough Sets Intell. Syst..

[4]  Yiyu Yao,et al.  Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model , 2009, Inf. Sci..

[5]  Fei-Yue Wang,et al.  A New Type of Covering Rough Set , 2006, 2006 3rd International IEEE Conference Intelligent Systems.

[6]  Randall Dougherty,et al.  Networks, Matroids, and Non-Shannon Information Inequalities , 2007, IEEE Transactions on Information Theory.

[7]  James G. Oxley,et al.  Matroid theory , 1992 .

[8]  Jianhua Dai,et al.  Approximations and uncertainty measures in incomplete information systems , 2012, Inf. Sci..

[9]  William Zhu,et al.  Geometric Lattice Structure of Covering-Based Rough Sets through Matroids , 2012, J. Appl. Math..

[10]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[11]  Guoyin Wang,et al.  Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing , 2013, Lecture Notes in Computer Science.

[12]  Qingxin Zhu,et al.  Quantitative analysis for covering-based rough sets through the upper approximation number , 2013, Inf. Sci..

[13]  Qingxin Zhu,et al.  Matroidal structure of rough sets and its characterization to attribute reduction , 2012, Knowl. Based Syst..

[14]  William Zhu,et al.  The vectorially matroidal structure of generalized rough sets based on relations , 2011, 2011 IEEE International Conference on Granular Computing.

[15]  Yuanping Zhang,et al.  Relationship Between Partition Matroid and Rough Set Through k-rank Matroid ⋆ , 2012 .

[16]  Witold Pedrycz,et al.  Positive approximation: An accelerator for attribute reduction in rough set theory , 2010, Artif. Intell..

[17]  Silvia Calegari,et al.  Granular computing applied to ontologies , 2010, Int. J. Approx. Reason..

[18]  Fei-Yue Wang,et al.  Relationships among three types of covering rough sets , 2006, 2006 IEEE International Conference on Granular Computing.

[19]  Yuhua Qian,et al.  Test-cost-sensitive attribute reduction , 2011, Inf. Sci..

[20]  H. Raiffa,et al.  Negotiation Analysis: The Science and Art of Collaborative Decision Making , 2003 .

[21]  Wei-Zhi Wu,et al.  A general approach to attribute reduction in rough set theory , 2007, Science in China Series F: Information Sciences.

[22]  T. A. Dowling,et al.  MATCHING THEORY FOR COMBINATORIAL GEOMETRIES , 1970 .

[23]  Jack Edmonds,et al.  Matroids and the greedy algorithm , 1971, Math. Program..

[24]  Roman Słowiński,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[25]  Fan Min,et al.  Dynamic Discretization: A Combination Approach , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[26]  Marzena Kryszkiewicz,et al.  Rough Set Approach to Incomplete Information Systems , 1998, Inf. Sci..

[27]  T. Y. Lin,et al.  Rough Sets and Data Mining , 1997, Springer US.

[28]  Zheng Pei,et al.  Generalized rough sets based on reflexive and transitive relations , 2008, Inf. Sci..

[29]  Yiyu Yao,et al.  "Rule + Exception" Strategies for Knowledge Management and Discovery , 2005, RSFDGrC.

[30]  William Zhu,et al.  Matroidal approaches to generalized rough sets based on relations , 2011, Int. J. Mach. Learn. Cybern..

[31]  James A. Mason,et al.  Discrete mathematics for computer science , 1992 .

[32]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[33]  Marzena Kryszkiewicz,et al.  Rules in Incomplete Information Systems , 1999, Inf. Sci..

[34]  Yiyu Yao,et al.  Relational Interpretations of Neigborhood Operators and Rough Set Approximation Operators , 1998, Inf. Sci..

[35]  William Zhu,et al.  Characteristic of partition-circuit matroid through approximation number , 2012, 2012 IEEE International Conference on Granular Computing.

[36]  Fei-Yue Wang,et al.  Reduction and axiomization of covering generalized rough sets , 2003, Inf. Sci..

[37]  Mao Hua,et al.  The Relation Between Matroid and Concept Lattice , 2006 .

[38]  Sanyang Liu,et al.  Matroidal approaches to rough sets via closure operators , 2012, Int. J. Approx. Reason..

[39]  William Zhu,et al.  Transversal and Function Matroidal Structures of Covering-Based Rough Sets , 2011, RSKT.

[40]  Fei-Yue Wang,et al.  Covering Based Granular Computing for Conflict Analysis , 2006, ISI.

[41]  Thomas Bittner,et al.  Rough Sets in Approximate Spatial Reasoning , 2000, Rough Sets and Current Trends in Computing.

[42]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[43]  Wei Wei,et al.  A comparative study of rough sets for hybrid data , 2012, Inf. Sci..

[44]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[45]  Nicole Fassbinder,et al.  Combinatorial Optimization Networks And Matroids , 2016 .

[46]  Meng Guang-wu LF-topological methods on the theory of covering generalized rough sets , 2008 .

[47]  William Zhu,et al.  Rough matroids based on relations , 2013, Inf. Sci..

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

[49]  William Zhu,et al.  Rough matroid , 2011, 2011 IEEE International Conference on Granular Computing.

[50]  Qinghua Hu,et al.  An efficient gene selection technique for cancer recognition based on neighborhood mutual information , 2010, Int. J. Mach. Learn. Cybern..

[51]  William Zhu,et al.  Matroidal Structure of Rough Sets Based on Serial and Transitive Relations , 2012, J. Appl. Math..

[52]  Qing Liu,et al.  Semantic analysis of rough logical formulas based on granular computing , 2006, 2006 IEEE International Conference on Granular Computing.

[53]  Alexander Sprintson,et al.  On the Index Coding Problem and Its Relation to Network Coding and Matroid Theory , 2008, IEEE Transactions on Information Theory.

[54]  Frantisek Matús Abstract Functional Dependency Structures , 1991, Theor. Comput. Sci..

[55]  Fei-Yue Wang,et al.  Axiomatic Systems of Generalized Rough Sets , 2006, RSKT.

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

[57]  Tao Feng,et al.  Reduction of rough approximation space based on matroid , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[58]  Urszula Wybraniec-Skardowska,et al.  Extensions and Intentions in the Ruogh Set Theory , 1998, Inf. Sci..

[59]  Yiyu Yao,et al.  Constructive and Algebraic Methods of the Theory of Rough Sets , 1998, Inf. Sci..

[60]  William Zhu,et al.  Topological approaches to covering rough sets , 2007, Inf. Sci..