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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2014

01.12.2014 | Original Article

Dynamic updating multigranulation fuzzy rough set: approximations and reducts

verfasst von: Hengrong Ju, Xibei Yang, Xiaoning Song, Yunsong Qi

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2014

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Abstract

As we all known, dynamic updating of rough approximations and reducts are keys to the applications of the rough set theory in real data sets. In recent years, with respect to different requirements, many approaches have been proposed to study such problems. Nevertheless, few of the them are carried out under multigranulation fuzzy environment. To fill such gap, the updating computations of multigranulation fuzzy rough approximations are explored in this paper. By considering the dynamic increasing of fuzzy granular structures, which are induced by fuzzy relations, naive and fast algorithms are presented, respectively. Moreover, both naive and fast forward greedy algorithms are designed for granular structure selection in dynamic updating environment. Experiments on six data sets from UCI show that fast algorithms are more effective for reducing computational time in comparison with naive algorithms.
Vorheriger Artikel Optimal revenue-sharing contract based on forecasting effort for uncertain agency problem

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Literatur
1.
Chan CC (1998) A rough set approach to attribute generalization in data mining. Inf Sci 107:169–176CrossRef
2.
Chen HM, Li TR, Qiao SJ, Ruan D (2010) A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. Int J Int Syst 25:1005–1026CrossRefMATH
3.
Chen HM, Li TR, Ruan D (2012) Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining. Knowl Based Syst 31:140–161CrossRef
4.
Cheng Y, Miao DQ (2011) The incremental method for fast computing the rough fuzzy approximations. Data Knowl Engin 70:84–100CrossRef
5.
Dai JH, Wang WT, Xu Q (2013) An uncertainty measure for incomplete decision tables and its applications. IEEE Trans on Cybern 43(4):1277–1289CrossRef
6.
Dai JH, Wang WT, Tian HW, Liu L (2013) Attribute selection based on a new conditional entropy for incomplete decision systems. Knowl based Syst 39:207–213CrossRef
7.
Dai JH, Tian HW, Wang WT, Liu L (2013) Decision rule mining using classification consistency rate. Knowl based Syst 43:95–102CrossRef
8.
Dai JH (2013) Rough set approach to incomplete numerical data. Inf Sci 241:43–57CrossRef
9.
Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2:107–122CrossRef
10.
Leung Y, Wu WA, Zhang WX (2006) Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Oper Res 168:164–180CrossRefMATHMathSciNet
11.
Li J, Han G, Wen J, Gao XB (2011) Robust tensor subspace learning for anomaly detection. Int J Mach Learn Cybern 2:89–98CrossRef
12.
Li HL, Chen MH (2008) Induction of multiple criteria optimal classification rules for biological and medical data. Comput Bio Med 38:42–52CrossRef
13.
Li TR, Ruan D, Geert W, Song J, Xu Y (2007) A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl Based Syst 20:485–494CrossRef
14.
Li SY, Li TR, Liu D (2013) Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set. Knowl Based Syst 40:17–26CrossRef
15.
Liang JY, Wang F, Dang CY, Qian YH (2012) A group incremental approach to feature selection applying rough set technique. IEEE Trans Knowl Data Engin. doi:10.​1109/​TKDE.​2012.​146
16.
Liu D, Li TR, Ruan D, Zou W (2009) An incremental approach for inducing knowledge from dynamic information systems. Fund Inf 94:245–260MATHMathSciNet
17.
Luo GZ, Yang XB (2010) Limited dominance-based rough set model and knowledge reductions in incomplete decision system. J Inf Sci Eng 26:2199–2211MathSciNet
18.
Pawlak Z (1992) Rough sets–theoretical aspects of reasoning about data. Kluwer Academic Publishers, Boston
19.
Qian YH, Liang JY (2006) Rough set method based on multi-granulations. In: 5th IEEE International Conference on cognitive informatics, pp 297–304
20.
Qian YH, Liang JY, Dang CY (2010) Incomplete multigranulation rough set. IEEE Trans Syst Man Cybern Part A 40:420–431CrossRef
21.
Qian YH, Liang JY, Wei W (2010) Pessimistic rough decision. In: Second International Workshop on rough sets theory, pp 440–449
22.
23.
Qian YH, Liang JY, Wu WZ, Dang CY (2011) Information granularity in fuzzy binary GrC model. IEEE Trans Fuzzy Syst 19:253–264CrossRef
24.
Shen LX, Loh HT (2004) Applying rough sets to market timing decisions. Decis Support Syst 37:583–597CrossRef
25.
Wang F, Liang JY, Qian YH (2013) Attribute reduction: a dimension incremental strategy. Knowl Based Syst 39:95–108CrossRef
26.
Wang F, Liang JY, Dang CY (2013) Attribute reduction for dynamic data sets. Appl Soft Comput 13:676–689CrossRef
27.
Wei L, Zhang WX (2005) Attribute reduction based on equivalence relation defined on attribute set and its power set. In: Dunin-Keplicz B, Jankowski A, Skowron A, Szczuka M (eds) International Workshop on monitoring, security and rescue Techniques in multiagent systems MSRAS 2004. Advances in soft computing, pp 317–326
28.
Wei L, Li HR, Zhang WX (2007) Knowledge reduction based on the equivalence relations defined on attribute set and its power set. Inf Sci 177:3178–3185CrossRefMATHMathSciNet
29.
Xu WH, Wang QR, Zhang XT (2011) Multi-granulation fuzzy rough sets in a fuzzy tolerance approximation space. Int J Fuzzy Syst 14:246–259MathSciNet
30.
Yang XB, Song XN, Dou HL, Yang JY (2011) Multi-granulation rough set: from crisp to fuzzy case. Ann Fuzzy Math Inf 1:55–70MathSciNet
31.
Yang XB, Song XN, Chen ZH, Yang JY (2012) On multigranulation rough sets in incomplete information system. Int J Mach Learn Cyber 3:223–232CrossRef
32.
Zhang JB, Li TR, Ruan D, Liu D (2012) Neighborhood rough sets for dynamic data mining. Int J Int Syst 27:317–342CrossRef
33.
Zhang JB, Li TR, Ruan D, Liu D (2012) Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems. Int J Approx Reason 53:620-635CrossRefMATHMathSciNet
Metadaten
Titel
Dynamic updating multigranulation fuzzy rough set: approximations and reducts
verfasst von
Hengrong Ju
Xibei Yang
Xiaoning Song
Yunsong Qi
Publikationsdatum
01.12.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0242-4

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