An incremental approach to attribute reduction of dynamic set-valued information systems

Set-valued information systems are important generalizations of single-valued information systems. In this paper, three relations are proposed for attribute reduction of set-valued information systems. Then, we convert a large-scale set-valued information system into a smaller relation information system. An incremental algorithm is designed to compress dynamic set-valued information systems. Concretely, we mainly address the compression updating from three aspects: variations of attribute set, immigration and emigration of objects and alterations of attribute values. Finally, several illustrative examples are employed to demonstrate that attribute reduction of dynamic set-valued information systems are simplified significantly by our proposed approaches.

[1]  Geert Wets,et al.  A rough sets based characteristic relation approach for dynamic attribute generalization in data mining , 2007, Knowl. Based Syst..

[2]  J. W. Grzymala-Busse,et al.  On rough sets and information system homomorphisms , 1988 .

[3]  Zhifei Zhang,et al.  International Journal of Approximate Reasoning Diverse Reduct Subspaces Based Co-training for Partially Labeled Data , 2022 .

[4]  Da Ruan,et al.  An Incremental Approach for Inducing Knowledge from Dynamic Information Systems , 2009, Fundam. Informaticae.

[5]  S. Nanda,et al.  Fuzzy rough sets , 1992 .

[6]  Jianhua Dai,et al.  Fuzzy rough set model for set-valued data , 2013, Fuzzy Sets Syst..

[7]  Yiyu Yao,et al.  Probabilistic approaches to rough sets , 2003, Expert Syst. J. Knowl. Eng..

[8]  Da Ruan,et al.  Incremental learning optimization on knowledge discovery in dynamic business intelligent systems , 2011, J. Glob. Optim..

[9]  Murat Diker,et al.  Textures and covering based rough sets , 2012, Inf. Sci..

[10]  Jing-Yu Yang,et al.  On multigranulation rough sets in incomplete information system , 2011, International Journal of Machine Learning and Cybernetics.

[11]  Andrea Capotorti,et al.  Credit scoring analysis using a fuzzy probabilistic rough set model , 2012, Comput. Stat. Data Anal..

[12]  W. Zakowski APPROXIMATIONS IN THE SPACE (U,π) , 1983 .

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

[14]  Zhiyong Xiao,et al.  Communicating between information systems based on including degrees , 2010, Int. J. Gen. Syst..

[15]  Jiye Liang,et al.  Set-valued ordered information systems , 2009, Inf. Sci..

[16]  Wojciech Ziarko,et al.  Probabilistic approach to rough sets , 2008, Int. J. Approx. Reason..

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

[18]  Jianhua Dai,et al.  Entropy measures and granularity measures for set-valued information systems , 2013, Inf. Sci..

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

[20]  Qiaoyan Wen,et al.  Some improved results on communication between information systems , 2010, Inf. Sci..

[21]  Li Deyu,et al.  Invariant characteristics of information systems under some homomorphisms. I , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[22]  Yiyu Yao,et al.  Attribute reduction in decision-theoretic rough set models , 2008, Inf. Sci..

[23]  Umberto Straccia,et al.  Generalized fuzzy rough description logics , 2012, Inf. Sci..

[24]  Tao Feng,et al.  Reductions of a fuzzy covering decision system , 2011, Int. J. Model. Identif. Control..

[25]  Guilong Liu,et al.  Rough set theory based on two universal sets and its applications , 2010, Knowl. Based Syst..

[26]  Xizhao Wang,et al.  Induction of multiple fuzzy decision trees based on rough set technique , 2008, Inf. Sci..

[27]  Nan Zhang,et al.  Graded rough set model based on two universes and its properties , 2012, Knowl. Based Syst..

[28]  Sudarsan Nanda,et al.  Fuzziness in rough sets , 2000, Fuzzy Sets Syst..

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

[30]  Xizhao Wang,et al.  Learning fuzzy rules from fuzzy samples based on rough set technique , 2007, Inf. Sci..

[31]  Yee Leung,et al.  Maximal consistent block technique for rule acquisition in incomplete information systems , 2003, Inf. Sci..

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

[33]  Andrzej Skowron,et al.  The rough sets theory and evidence theory , 1990 .

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

[35]  Liang Liu,et al.  Attribute selection based on a new conditional entropy for incomplete decision systems , 2013, Knowl. Based Syst..

[36]  Yanyong Guan,et al.  Set-valued information systems , 2006, Inf. Sci..

[37]  Da Ruan,et al.  Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems , 2012, Int. J. Approx. Reason..

[38]  Dan Meng,et al.  Soft rough fuzzy sets and soft fuzzy rough sets , 2011, Comput. Math. Appl..

[39]  Dun Liu,et al.  Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set , 2013, Knowl. Based Syst..

[40]  Jianhua Dai,et al.  Rough set approach to incomplete numerical data , 2013, Inf. Sci..

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

[42]  Ming Zhang,et al.  Neighborhood systems-based rough sets in incomplete information system , 2011, Knowl. Based Syst..

[43]  Shaojie Qiao,et al.  A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values , 2010 .

[44]  Bing Huang,et al.  Dominance-based rough set model in intuitionistic fuzzy information systems , 2012, Knowl. Based Syst..

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

[46]  J. Grzymala-Busse Rough set and CART approaches to mining incomplete data , 2010, 2010 International Conference of Soft Computing and Pattern Recognition.

[47]  Qingguo Li,et al.  Reduction about approximation spaces of covering generalized rough sets , 2010, Int. J. Approx. Reason..

[48]  Rajen B. Bhatt,et al.  On the compact computational domain of fuzzy-rough sets , 2005, Pattern Recognit. Lett..

[49]  Degang Chen,et al.  A systematic study on attribute reduction with rough sets based on general binary relations , 2008, Inf. Sci..

[50]  Da Ruan,et al.  Neighborhood rough sets for dynamic data mining , 2012, Int. J. Intell. Syst..

[51]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

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

[53]  Dominik Slezak,et al.  The investigation of the Bayesian rough set model , 2005, Int. J. Approx. Reason..

[54]  Li Deyu,et al.  Invariant characters of information systems under some homomorphisms , 2000 .

[55]  Sankar K. Pal,et al.  Roughness of a Fuzzy Set , 1996, Inf. Sci..

[56]  Qin Ke-yun Attribute Reduction of Set-valued Information System Based on Tolerance Relation , 2009 .

[57]  Decui Liang,et al.  Incorporating logistic regression to decision-theoretic rough sets for classifications , 2014, Int. J. Approx. Reason..

[58]  Ping Zhu,et al.  Covering rough sets based on neighborhoods: An approach without using neighborhoods , 2009, Int. J. Approx. Reason..

[59]  Da Ruan,et al.  Probabilistic model criteria with decision-theoretic rough sets , 2011, Inf. Sci..

[60]  Jianhua Dai,et al.  Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification , 2013, Appl. Soft Comput..