A Parallel Matrix-Based Approach for Computing Approximations in Dominance-Based Rough Sets Approach

Dominance-based Rough Sets Approach (DRSA) is a useful tool for multi-criteria classification problems solving. Parallel computing is an efficient way to accelerate problems solving. Computation of approximations is a vital step to find the solutions with rough sets methodologies. In this paper, we propose a matrix-based approach for computing approximations in DRSA and design the corresponding parallel algorithms on Graphics Processing Unit (GPU). A numerical example is employed to illustrate the feasibility of the matrix-based approach. Experimental evaluations show the performance of the parallel algorithm.

[1]  Hongmei Chen,et al.  Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization , 2014, Inf. Sci..

[2]  Francisco Herrera,et al.  Combining Numerical and Linguistic Information in Group Decision Making , 1998, Inf. Sci..

[3]  Nancy Hitschfeld-Kahler,et al.  A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures , 2014 .

[4]  Yi Cheng,et al.  The incremental method for fast computing the rough fuzzy approximations , 2011, Data Knowl. Eng..

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

[6]  Jianhui Lin,et al.  A Rough-Set-Based Incremental Approach for Updating Approximations under Dynamic Maintenance Environments , 2013, IEEE Transactions on Knowledge and Data Engineering.

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

[8]  Dun Liu,et al.  Dynamic Maintenance of Approximations in Dominance‐Based Rough Set Approach under the Variation of the Object Set , 2013, Int. J. Intell. Syst..

[9]  Tianrui Li,et al.  Composite rough sets for dynamic data mining , 2014, Inf. Sci..

[10]  Yi Pan,et al.  International Journal of Approximate Reasoning a Comparison of Parallel Large-scale Knowledge Acquisition Using Rough Set Theory on Different Mapreduce Runtime Systems , 2022 .

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

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

[13]  Dun Liu,et al.  Incremental approaches for updating approximations in set-valued ordered information systems , 2013, Knowl. Based Syst..

[14]  Andrzej Skowron,et al.  Rough sets: Some extensions , 2007, Inf. Sci..

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

[16]  Da Ruan,et al.  A parallel method for computing rough set approximations , 2012, Inf. Sci..

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

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

[19]  Chien-Chung Chan,et al.  A Rough Set Approach to Attribute Generalization in Data Mining , 1998, Inf. Sci..