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A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets

  • Mathematical methods in data science
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Abstract

The data we need to deal with are getting bigger and bigger in recent years, and the same happens to multi-granulation rough sets, so updated schemes have been proposed with the variation of attributes or attribute values in multi-granulation rough sets. This paper puts forward a dynamic mechanism to update the approximations in multi-granulation rough sets when increasing or decreasing objects. Firstly, the relationships between the original approximations and updated approximations are explored when adding or deleting objects and the dynamic processes of updating the lower and upper approximations in optimistic and pessimistic approximations are proposed. Secondly, two corresponding dynamic algorithms and their time complexity are given. Finally, the experimental evaluations show the effectiveness of the proposed dynamic updating algorithms compared with the static algorithm.

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References

  • Alcantud JCR, Zhan JM (2020) Multi-granular soft rough covering sets. Soft Computing 24(13):9391–9402

    Article  MATH  Google Scholar 

  • Ananthanarayana VS, Narasimha Murty M, Subramanian DK (2003) Tree structure for efficient data mining using rough sets, Pattern Recognition Letters, 24:851-862

    Article  MATH  Google Scholar 

  • Chen HM, Li TR, Qiao SJ (2010) A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. International Journal of Intelligent Systems 25:1005–1026

    Article  MATH  Google Scholar 

  • Chen HM, Li TR, Ruan D (2012) Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining. Knowledge-Based Systems 31(7):140–161

    Article  Google Scholar 

  • Chen HM, Li TR, Ruan D, Lin JH, Hu CX (2013) A rough-set based incremental approach for updating approximations under dynamic maintenance environments. IEEE Trans-actions on Knowledge and Data Engineering 25(2):274–284

    Article  Google Scholar 

  • Chen HM, Li TR, Luo C, Horng SJ, Wang GY (2014) A rough set-based method for updating decision rules on attribute values coarsening and refining. IEEE Transactions on Knowledge and Data Engineering 26(12):2886–2899

    Article  Google Scholar 

  • Cheng Y (2011) The incremental method for fast computing the rough fuzzy approximations. Data & Knowledge Engineering 70(1):84–100

    Article  Google Scholar 

  • Das AK, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl. Soft Computing 65:400–401

    Article  Google Scholar 

  • Fan Y, Tseng T, Chem C, Huang C (2009) Rule induction based on an incremental rough set. Expert Systems with Applications 36(9):11439–11450

    Article  Google Scholar 

  • Feng T, Mi JS (2016) Variable precision multigranulation decision-theoretic fuzzy rough sets. Knowl-Based Syst 91:93–101

    Article  Google Scholar 

  • Hu CX, Zhang L (2020) Efficient approaches for maintaining dominance-based multigranulation approximations with incremental granular structures. Int J Approx Reason 126:202–227

    Article  MathSciNet  MATH  Google Scholar 

  • Hu J, Li TR, Chen HM, Zeng AP (2015) An incremental learning approach for updating approximations in rough set model over dual universes. International Journal of Intelligent Systems 30(8):923–947

    Article  Google Scholar 

  • Hu CX, Liu SX, Liu GX (2017) Matrix-based approaches for dynamic updating approximations in multigranulation rough sets. Knowledge-Based Systems 122:51–63

    Article  Google Scholar 

  • Hu CX, Liu SX, Huang XL (2017) Dynamic updating approximations in multigranulation rough sets while refining or coarsening attribute values. Knowledge-Based Systems 130:62–73

  • Hu CX, Zhang L, Liu SX (2021) Incremental approaches to update multigranulation approximations for dynamic information systems. Journal of Intelligent and Fuzzy 40(3):4661–4682

    Article  Google Scholar 

  • Huang B, Guo CX, Zhuang YL, Li HX, Zhou XZ (2014) Intuitionistic fuzzy multigranulation rough sets. Information Sciences 277:299–320

    Article  MathSciNet  MATH  Google Scholar 

  • Jerzy B, Slowinski R (2003) Incremental induction of decision rules from dominance-based rough approximations. Electronic Notes in Theoretical Computer Science 82:40–51

    Article  MATH  Google Scholar 

  • Lang GM, Miao DQ, Cai MJ, Zhang ZF (2017) Incremental approaches for updating reducts in dynamic covering information systems. Knowledge-Based Systems 134:85–104

    Article  Google Scholar 

  • Li SY, Li TR (2015) Incremental update of approximations in dominance-based rough sets approach under the variation of attribute values. Information Sciences 294:348–361

    Article  MathSciNet  MATH  Google Scholar 

  • Li SY, Li TR, Liu D (2013) Dynamic maintenance of approximations in dominance-based rough set approach under the variation of the object set. International Journal of Intelligent Systems 28:729–751

    Article  Google Scholar 

  • Li SY, Li TR, Liu D (2013) Incremental updating approximations in dominance-based rough sets approach under the variation of the attirbute set. Knowledge-Based Systems 40:17–26

    Article  Google Scholar 

  • Lin GP, Qian YH, Li JJ (2012) NMGRS:Neighborhood-based multigranulation rough sets. International Journal of Approximate Reasoning 53(7):1080–1093

    Article  MathSciNet  MATH  Google Scholar 

  • Luo C, Li TR, Chen HM, Fujita H, Yi Z (2016) Efficient updating of probabilistic approximations with incremental objects. Knowledge-Based Systems 109:71–83

    Article  Google Scholar 

  • Luo C, Li TR, Chen HM, Lu LX (2016) Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values. Information Sciences 299:221–242

    Article  MathSciNet  MATH  Google Scholar 

  • Pawlak Z (1982) Rough sets. International Journal of Computer and Information Sciences 11(5):341–356

    Article  MATH  Google Scholar 

  • Peters JF, Skowron A (2002) A rough set approach to knowledge discovery. International Journal of Intelligent Systems 17(2):109–112

    Article  Google Scholar 

  • Qian YH, Liang JY, Yao YY, Dang CY (2010) MGRS: A multi-granulation rough set. Information Sciences 180:949–970

    Article  MathSciNet  MATH  Google Scholar 

  • Qian YH, Liang JY, Dang CY (2010) Incomplete multigranulation rough set. IEEE Transactions on Systems Man and Cybernetics Part A 40(2):420–431

    Article  Google Scholar 

  • Qian YH, Zhang H, Sang YL, Liang JY (2014) Multigranulation decision-theoretic rough sets. Int J Approx Reason 55(1):225–237

    Article  MathSciNet  MATH  Google Scholar 

  • Qian YH, Liang XY, Lin GP, Guo Q, Liang JY (2017) Local multigranulation decision-theoretic rough sets. International Journal of Approximate Reasoning 82:119–137

    Article  MathSciNet  MATH  Google Scholar 

  • Shu WH, Qian WB, Xie YH (2019) Incremental approaches for feature selection from dynamic data with the variation of multiple objects. Knowledge-Based Systems 163:320–331

    Article  Google Scholar 

  • A. Skowron, J. Stepaniuk, R.Swiniarski, Approximation spaces in rough granular computing, Fundamenta Informaticae, 100(2010), 141–157

    Article  MathSciNet  MATH  Google Scholar 

  • J. Stepaniu, Relational data and rough sets, Fundamenta Informaticae, 79(2007), 525–539

    MathSciNet  Google Scholar 

  • Stepaniuk J, Kierzkowska K (2003) Hybrid classifier based on rough sets and neural networks. Electronic Notes in Theoretical Computer Science 82:228–238

    Article  MATH  Google Scholar 

  • Wang F, Liang JY, Dang CY (2013) Attribute reduction for dynamic data sets. Applied Soft Computing 13(1):676–689

    Article  Google Scholar 

  • W.H. Xu, W.T.Wang, X.T.Zhang, Generalized multigranulation rough sets and optimal granularity selection, Granular Computing, 2(2017), 271–288

  • Yang XB, Qi Y, Yu HL, Song XN, Yang JY (2014) Updating multigranulation rough approximations with increasing of granular structures. Knowledge-Based Systems 64:59–69

    Article  Google Scholar 

  • Zeng AP, Li TR, Hu J, Chen HM, Luo C (2017) Dynamical updating fuzzy rough approximations for hybrid data under the variation of attribute values. Information Sciences 378:363–388

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang J, Li TR, Ruan D, Liu D (2012) Neighborhood rough sets for dynamic data mining. International Journal of Intelligence Systems 27:317–342

    Article  Google Scholar 

  • Zhang JB, Li TR, Ruan D, Liu D (2012) Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems. International Journal of Approximate Reasoning 53(4), 620–635

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Acknowledgements

This work is supported by the Nature Science Foundation of Shanxi Province (No. 201901D111280).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Shanxi Normal University, Linfen, 041000, Shanxi, People’s Republic of China

    Hong Wang & Jingtao Guan

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  1. Hong Wang
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  2. Jingtao Guan
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Correspondence to Hong Wang.

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Wang, H., Guan, J. A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets. Soft Comput 27, 5257–5276 (2023). https://doi.org/10.1007/s00500-023-07886-7

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  • DOI: https://doi.org/10.1007/s00500-023-07886-7

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