Top

Soft Computing

Published in:

10-02-2020 | Foundations

Generic extended multigranular sets for mixed and incomplete information systems

Authors: Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, José Luis Velázquez-Rodríguez

Published in: Soft Computing | Issue 8/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Granular computing is a widely used computational paradigm nowadays. Particularly, within the rough set theory, granular computing plays a key role. In this paper, we propose a generic approach of rough sets, the granular extended multigranular sets (GEMS) for dealing with both mixed and incomplete information systems. Not only our proposal does use the traditional optimistic and pessimistic granulations with respect to single attributes, but also we introduce granulations with respect to attribute sets, as well as two new ways of granulating: the optimistic + pessimistic granulation and the pessimistic + optimistic granulation. In addition, we have developed a particular case of the proposed GEMS: the multigranular maximum similarity rough sets (MMSRS). We have proved some of the properties of the MMSRS, and we tested its effectiveness with respect to other existing granular rough sets models. The experimental results show the flexibility and the capabilities of the proposed model, while handling mixed and incomplete information systems.

previous article Fast clustering-based weighted twin support vector regression

next article An enhanced heuristic ant colony optimization for mobile robot path planning

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Amin A, Anwar S, Adnan A, Nawaz M, Alawfi K, Hussain A, Huang K (2017) Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing 237:242–254. https://doi.org/10.1016/j.neucom.2016.12.009 CrossRef

Antón-Vargas JA, Villuendas-Rey Y, Yáñez-Márquez C, López-Yáñez I, Camacho-Nieto O (2018) Improving the performance of an associative classifier by gamma rough sets based instance selection. Int J Pattern Recogn 32(01):1860009. https://doi.org/10.1142/S0218001418600091 CrossRef

Attia AH, Sherif AS, El-Tawel GS (2016) Maximal limited similarity-based rough set model. Soft Comput 20(8):3153–3161. https://doi.org/10.1007/s00500-016-2243-6 CrossRefMATH

Chaudhuri SB, Rahman M (2018) Design of a medical expert system (MES) based on rough set theory for detection of cardiovascular diseases. In: Saeed K, Chaki N, Pati B, Bakshi S, Mohapatra D (eds) Progress in advanced computing and intelligent engineering advances in intelligent systems and computing, vol 563. Springer, Singapore, pp 325–332CrossRef

Cheng X, Long R, Chen H, Li W (2018) Green competitiveness evaluation of provinces in China based on correlation analysis and fuzzy rough set. Ecol Indic 85:841–852. https://doi.org/10.1016/j.ecolind.2017.11.045 CrossRef

Dai J, Hu Q, Hu H, Huang D (2018) Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans Fuzzy Syst 26(2):937–950. https://doi.org/10.1109/TFUZZ.2017.2698420 CrossRef

Dutta S, Ghatak S, Dey R, Das AK, Ghosh S (2018) Attribute selection for improving spam classification in online social networks: a rough set theory-based approach. Soc Net Anal Min 8(1):7. https://doi.org/10.1007/s13278-017-0484-8 CrossRef

Grzymala-Busse JW (2005) Characteristic relations for incomplete data: a generalization of the indiscernibility relation. In: Skowron A, Peters JF (eds) Transactions on rough sets IV. Lecture notes in computer science, vol 3700. Springer, Berlin, pp 58–68CrossRef

Hu QH, Xie ZX, Yu DR (2007) Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recogn 40(12):3509–3521. https://doi.org/10.1016/j.patcog.2007.03.017 CrossRefMATH

Hu QH, Liu JF, Yu DR (2008a) Mixed feature selection based on granulation and approximation. Knowl Based Syst 21(4):294–304. https://doi.org/10.1016/j.knosys.2007.07.001 CrossRef

Hu QH, Yu DR, Liu JF, Wu CX (2008b) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594. https://doi.org/10.1016/j.ins.2008.05.024 MathSciNetCrossRefMATH

Huang S, Li M (2014) Limited and variable precision rough set model. J Inf Comput Sci 11(10):3493–3501. https://doi.org/10.12733/jics20103983 CrossRef

Kryszkiewicz M (1998) Rough set approach to incomplete information systems. Inf Sci 112(1–4):39–49. https://doi.org/10.1016/S0020-0255(98)10019-1 MathSciNetCrossRefMATH

Kryszkiewicz M (1999) Rules in incomplete information systems. Inf Sci 113(3–4):271–292. https://doi.org/10.1016/S0020-0255(98)10065-8 MathSciNetCrossRefMATH

Liang DC, Xu ZS, Liu D (2017) Three-way decisions with intuitionistic fuzzy decision-theoretic rough sets based on point operators. Inf Sci 375:183–201. https://doi.org/10.1016/j.ins.2016.09.039 CrossRefMATH

Lin GP, Qian YH, Li JJ (2012) NMGRS: neighborhood-based multigranulation rough sets. Int J Approx Reason 53(7):1080–1093. https://doi.org/10.1016/j.ijar.2012.05.004 MathSciNetCrossRefMATH

Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356. https://doi.org/10.1007/BF01001956 CrossRefMATH

Pawlak Z, Skowron A (2007) Rough sets: some extensions. Inf Sci 177(1):28–40. https://doi.org/10.1016/j.ins.2006.06.006 MathSciNetCrossRefMATH

Pedrycz W (2001) Granular computing: an introduction. In: Proceedings IEEE joint 9th IFSA world congress and 20th NAFIPS international conference. https://doi.org/10.1109/NAFIPS.2001.943745

Qian YH, Liang JY (2006) Rough set method based on multi-granulations. In: Proceedings 5th IEEE international conference on cognitive informatics. https://doi.org/10.1109/COGINF.2006.365510

Qian Y, Liang J, Dang C (2010) Incomplete multigranulation rough set. IEEE Trans Syst Man Cybern 40(2):420–431. https://doi.org/10.1109/TSMCA.2009.2035436 CrossRef

Qian Y, Zhang H, Sang Y, Liang J (2014) Multigranulation decision-theoretic rough sets. Int J Approx Reason 55(1):225–237. https://doi.org/10.1016/j.ijar.2013.03.004 MathSciNetCrossRefMATH

Saltos R, Weber R (2016) A rough-fuzzy approach for support vector clustering. Inf Sci 339:353–368. https://doi.org/10.1016/j.ins.2015.12.035 CrossRef

Slowinski R, Vanderpooten D (2000) A generalized definition of rough approximations based on similarity. IEEE Trans Knowl Data Eng 12(2):331–336. https://doi.org/10.1109/69.842271 CrossRef

Stefanowski J, Tsoukiàs A (1999) On the extension of rough sets under incomplete information. In: Zhong N, Skowron A, Ohsuga S (eds) New directions in rough sets, data mining, and granular-soft computing. Lecture notes in computer science, vol 1711. Springer, Berlin, pp 73–81CrossRef

Stefanowski J, Tsoukiàs A (2001) Incomplete information tables and rough classification. Comput Intell 17(3):545–566. https://doi.org/10.1111/0824-7935.00162 CrossRefMATH

Sun B, Ma W, Qian Y (2017) Multigranulation fuzzy rough set over two universes and its application to decision making. Knowl-Based Syst 123:61–74. https://doi.org/10.1016/j.knosys.2017.01.036 CrossRef

Van Nguyen D, Yamada K, Unehara M (2013) Extended tolerance relation to define a new rough set model in incomplete information systems. Adv Fuzzy Syst 2013:372091. https://doi.org/10.1155/2013/372091 MathSciNetCrossRefMATH

Villuendas-Rey Y (2018) Maximal similarity granular rough sets for mixed and incomplete information systems. Soft Comput. https://doi.org/10.1007/s00500-018-3408-2 CrossRefMATH

Wang G, Guan L, Hu F (2008) Rough set extensions in incomplete information systems. Front Electr Electron Eng China 3(4):399–405. https://doi.org/10.1155/2013/372091 CrossRef

Yang X, Yang J, Yu D, Wu C (2008) Rough set model based on variable parameter classification in incomplete information systems. Syst Eng Theory Pract 5:015

Yang X, Xu S, Dou H, Song X, Yu H, Yang J (2017) Multigranulation rough set: a multiset based strategy. Int J Comput Inf Syst 10:277–292. https://doi.org/10.2991/ijcis.2017.10.1.19 CrossRef

Yao YY (2001) Information granulation and rough set approximation. Int J Intell Syst 16(1):87–104. https://doi.org/10.1002/1098-111X(200101)16:1%3c87:AID-INT7%3e3.0.CO;2-S CrossRefMATH

Yu W, Zhang Z, Zhong Q, Sun L (2017) Extended TODIM for multi-criteria group decision making based on unbalanced hesitant fuzzy linguistic term sets. Comput Ind Eng 114:316–328CrossRef

Yu W, Zhang Z, Zhong Q (2019) Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach. Ann Oper Res. https://doi.org/10.1007/s10479-019-03432-7 CrossRef

Zadeh LA (1979) Fuzzy sets and information granularity. In: Gupta M, Ragade R, Yager R (eds) Advances in fuzzy set theory and applications. North-Holland, Amsterdam, pp 3–18

Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127. https://doi.org/10.1016/S0165-0114(97)00077-8 MathSciNetCrossRefMATH

Zhang Z, Kou X, Yu W, Guo C (2018) On priority weights and consistency for incomplete hesitant fuzzy preference relations. Knowl Based Syst 143:115–126CrossRef

Zhang Z, Yu W, Martinez L, Gao Y (2019) Managing multigranular unbalanced hesitant fuzzy linguistic information in multiattribute large-scale group decision making: a linguistic distribution-based approach. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2019.2949758 CrossRef

Title: Generic extended multigranular sets for mixed and incomplete information systems
Authors: Yenny Villuendas-Rey
Cornelio Yáñez-Márquez
José Luis Velázquez-Rodríguez
Publication date: 10-02-2020
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 8/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04748-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 8/2020

An efficient index structure for distributed k-nearest neighbours query processing

Small-scale moving target detection in aerial image by deep inverse reinforcement learning

Study on a storage location strategy based on clustering and association algorithms

A watermarking scheme based on rotating vector for image content authentication

Wajsberg algebras arising from binary block codes

On one-time cookies protocol based on one-time password

Premium Partner