Incremental mechanism of attribute reduction based on discernible relations for dynamically increasing attribute

  • Degang Chen
  • Lianjie DongEmail author
  • Jusheng Mi


Rough set is a data evaluation methodology to take care of uncertainty in data. Attribute reduction with rough set goals to achieve a compact and informative attribute set for a given data sets, and incremental mechanism is reasonable selection for attribute reduction in dynamic data sets. This paper focuses on introducing incremental mechanism to develop effective incremental algorithm during the arrival of new attributes in terms of approach of discerning samples. The traditional definition of discernibility matrix is improved first to address fewer samples to be discerned. Based on this improvement, discernible relation is developed for every attribute and utilized to characterize attribute reduction. For dynamic data sets with the dynamically increasing of attributes, an incremental mechanism is introduced to judge and ignore unnecessary new arriving attributes. For necessary new arriving attributes, the original reduct is updated in terms of updating of discernible relations instead of information granular or information entropy. The efficiency and effectiveness of developed incremental algorithm based on this mechanism is demonstrated through experimental comparisons in this paper in terms of running time.


Rough set Attribute reduction Discernible relation Incremental mechanism 



This work is supported by the fund of North China Electric Power University, National Key R&D Program of China and the Fundamental Research Funds for the Central Universities (2018YFC0831404, 2018YFC0830605, 2018QN050).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human or animals performed by any of the authors.


  1. Benitez-Caballero MJ, Medina J, Ramirez-Poussa E, Slȩzak D (2018) Bireducts with tolerance relations. Inf Sci 435:26–39MathSciNetCrossRefGoogle Scholar
  2. Benitez-Caballero MJ, Medina J, Ramirez-Poussa E, Slezak D (2019) A computational procedure for variable selection preserving different initial conditions. Int J Comput Math. CrossRefGoogle Scholar
  3. Bien Z (2007) Incremental inductive learning algorithm in the framework of rough set theory and its application. Int J Fuzzy Syst 1(1):25–36MathSciNetGoogle Scholar
  4. Chen D, Yang Y (2014) Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models. IEEE Trans Fuzzy Syst 22(5):1325–1334MathSciNetCrossRefGoogle Scholar
  5. Chen D, Zhao S, Zhang L, Yang Y, Zhang X (2012) Sample pair selection for attribute reduction with rough set. IEEE Trans Knowl Data Eng 24(11):2080–2093CrossRefGoogle Scholar
  6. Feng H, Jin D, Guo Y (2007) Incremental algorithms for attribute reduction in decision table. Control Decis 22(3):267–268Google Scholar
  7. Guan L, Wang G (2010) An incremental updating algorithm for attribute reduction set of decision tables. J Front Comput Sci Technol 4(5):436–444Google Scholar
  8. Han Y, Shi P, Chen S (2015) Bipolar-valued rough fuzzy set and its applications to decision information system. IEEE Trans Fuzzy Syst 23:2358–2370CrossRefGoogle Scholar
  9. Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594MathSciNetCrossRefGoogle Scholar
  10. Jing Y, Li T, Huang J, Zhang Y (2016) An incremental attribute reduction approach based on knowledge granularity under the attribute generalization. Int J Approx Reason 76:80–95MathSciNetCrossRefGoogle Scholar
  11. Liang J, Wang F, Dang C, Qian Y (2014) A group incremental approach to feature selection applying rough set technique. IEEE Trans Knowl Data Eng 26(2):294–308CrossRefGoogle Scholar
  12. Medina J (2012) Relating attribute reduction in formal, object-oriented and property-oriented concept lattices. Comput Math Appl 64(6):1992–2002MathSciNetCrossRefGoogle Scholar
  13. Orlowska M, Orlowski M (1992) Maintenance of knowledge in dynamic information systems. Intelligent decision support. Springer, NetherlandsGoogle Scholar
  14. Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11(5):341–356CrossRefGoogle Scholar
  15. Shu W, Shen H (2014) Updating attribute reduction in incomplete decision systems with the variation of attribute set. Elsevier, AmsterdamCrossRefGoogle Scholar
  16. Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. In: Słowiński R (ed) Intelligent decision support. Theory and decision library (Series D: System theory, knowledge engineering and problem solving), vol 11. Springer, Dordrecht, pp 331–362CrossRefGoogle Scholar
  17. Stawicki S, Sleak D, Janusz A, Widz S (2017) Decision bireducts and decision reducts—a comparison. Int J Approx Reason 84:75–109MathSciNetCrossRefGoogle Scholar
  18. Swiniarski R, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recogn Lett 24(6):833–849CrossRefGoogle Scholar
  19. Teng S, Liu M, Yang A, Zhang J, Nian Y, He M (2016) Efficient attribute reduction from the viewpoint of discernibility. Inf Sci 326:297–314MathSciNetCrossRefGoogle Scholar
  20. Wang F, Liang J, Dang C (2013a) Attribute reduction for dynamic data sets. Appl Soft Comput 13(1):676–689CrossRefGoogle Scholar
  21. Wang F, Liang J, Qian Y (2013b) Attribute reduction: a dimension incremental strategy. Knowl Based Syst 39:95–108CrossRefGoogle Scholar
  22. Yao Y, Zhao Y (2009) Discernibility matrix simplification for constructing attribute reducts. Inf Sci 179(7):867–882MathSciNetCrossRefGoogle Scholar
  23. Zeng A, Li T, Liu D, Zhang J, Chen H (2015) A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst 258:39–60MathSciNetCrossRefGoogle Scholar
  24. Ziarko W (1993) Variable precision rough set model. J Comput Syst Sci 46(1):39–59MathSciNetCrossRefGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and PhysicsNorth China Electric Power UniversityBeijingChina
  2. 2.School of Control and Computer EngineeringNorth China Electric Power UniversityBeijingChina
  3. 3.College of ScienceHebei Agricultural UniversityBaodingChina
  4. 4.College of Mathematics and Information ScienceHebei Normal UniversityShijiazhuangChina

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