Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
The Born and Growing of Chinese Rough Set Community with Help of Professor Zdzisław Pawlak Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Recent Development of Rough Computing: A Scientometrics View

verfasst von : Jing Tao Yao, Adeniyi Onasanya

Erschienen in: Thriving Rough Sets

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The rough set theory has been gaining popularity among researchers and scholars since its inception in 1982. We present this research in commemorating the father of rough sets, Professor Zdzisław Pawlak, and celebrating the 90th anniversary of his birth. Scientometrics is the science that quantitatively measures and analyzes sciences. We use scientometrics approach to quantitatively analyze the contents and citation trends of rough set research. The results presented in this chapter are a follow-up of Yao and Zhang’s work published in 2013. We first identify prolific authors, impact authors, impact research groups, and the most impact papers based on Web of Science database. We provide comparison with previous results and analyses of the changes. We further examine features of journal articles and conference papers in terms of research topics and impacts. The third part of the chapter is to examine highly cited papers identified by Web of Science as top 1% based on the academic field and publication year. In the fourth part, we investigate the top journals of rough set publications. There are some interesting results in key indicators between 2013 and 2016 results, for instance, the number of papers published increased by 35%, the total citations increased by 83%, and the h-index values increased by over 32%, while the average citation per paper increased by about 36%. We also found that the number of publications in the recent 5 years was about one third of the total number of rough set publications. This further indicates that rough sets as a research domain is attracting more researchers and growing healthily.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Zdzisław Pawlak as I Saw Him and Remember Him Now
Nächstes Kapitel Rough Sets, Rough Mereology and Uncertainty
Literatur
1.
2.
Ali, M.I.: A note on soft sets, rough soft sets and fuzzy soft sets. Appl. Soft Comput. 11(4), 3329–3332 (2011)CrossRef
3.
Azam, N., Yao, J.T.: Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets. Int. J. Approx. Reason. 55(1), 142–155 (2014)MathSciNetMATHCrossRef
4.
Blaszczynski, J., Slowinski, R., Szelag, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf. Sci. 181(5), 987–1002 (2011)MathSciNetCrossRef
5.
Bonikowski, Z., Bryniarski, E., Wybraniec-Skardowska, U.: Extensions and intentions in the rough set theory. Inf. Sci. 107(1–4), 149–167 (1998)MathSciNetMATHCrossRef
6.
Chen, H.L., Yang, B., Liu, J., Liu, D.Y.: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl. 38(7), 9014–9022 (2011)CrossRef
7.
Chen, H.L., Yang, B., Wang, G., Liu, J., Xu, X.: Wang, SJ; Liu, DY: A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowl.-Based Syst. 24(8), 1348–1359 (2011)CrossRef
8.
Chen, Y., Miao, D., Wang, R., Wu, K.: A rough set approach to feature selection based on power set tree. Knowl.-Based Syst. 24(2), 275–281 (2011)CrossRef
9.
10.
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. General Syst. 17(2–3), 191–209 (1990)MATHCrossRef
11.
Dubois, D., Prade, H.: Gradualness, uncertainty and bipolarity. Making sense of fuzzy sets. Fuzzy Sets Syst. 192, 3–24 (2012)MathSciNetMATHCrossRef
12.
13.
14.
Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation of a preference relation by dominance relations. Eur. J. Oper. Res. 117(1), 63–83 (1999)MATHCrossRef
15.
Herbert, J.P., Yao, J.T.: Game-theoretic rough sets. Fundamenta Informaticae 108(3–4), 267–286 (2011)MathSciNetMATH
16.
Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Nat. Acad. Sci. 46, 16569 (2005)MATHCrossRef
17.
18.
Jia, X.Y., Tang, Z., Liao, W., Shang, L.: Minimum cost attribute reduction in decision-theoretic rough set models. Inf. Sci. 219, 151–167 (2013)MathSciNetMATHCrossRef
19.
Jia, X.Y., Tang, Z., Liao, W., Shang, L.: On an optimization representation of decision-theoretic rough set model. Int. J. Approx. Reason. 55(1), 156–166 (2014)MathSciNetMATHCrossRef
20.
Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2009)CrossRef
21.
22.
23.
Li, W.T., Xu, W.H.: Double-quantitative decision-theoretic rough set. Inf. Sci. 316, 54–67 (2015)CrossRef
24.
Li, H., Zhou, X.: Risk decision making based on decision-theoretic rough set: a three-way view decision model. Int. J. Comput. Intell. Syst. 4(1), 1–11 (2011)MathSciNetCrossRef
25.
Li, J., Mei, C., Lv, Y.: Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int. J. Approx. Reason. 54(1), 149–165 (2013)MathSciNetMATHCrossRef
26.
Li, J.H., Mei, C.L., Xu, W.H., Qian, Y.H.: Concept learning via granular computing: a cognitive viewpoint. Inf. Sci. 298, 447–467 (2015)MathSciNetMATHCrossRef
27.
Liang, D.C., Liu, D.: Systematic studies on three-way decisions with interval-valued decision theoretic rough sets. Inf. Sci. 276, 186–203 (2014)CrossRef
28.
Liang, D., Liu, D., Pedrycz, W., Hu, P.: Triangular fuzzy decision-theoretic rough sets. Int. J. Approx. Reason. 54(8), 1087–1106 (2013)MATHCrossRef
29.
Liang, D.C., Pedrycz, W., Liu, D., Hu, P.: Three-way decisions based on decision-theoretic rough sets under linguistic assessment with the aid of group decision making. Appl. Soft Comput. 29, 256–269 (2015)CrossRef
30.
Liang, J., Wang, F., Dang, C., Qian, Y.: An efficient rough feature selection algorithm with a multi-granulation view. Int. J. Approx. Reason. 53(6), 912–926 (2012)MathSciNetCrossRef
31.
Lin, G., Qian, Y., Li, J.: NMGRS: Neighborhood-based multigranulation rough sets. Int. J. Approx. Reason. 53(7), 1080–1093 (2012)MathSciNetMATHCrossRef
32.
Li, N., Zhou, R., Hu, Q., Liu, X.: Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine. Mech. Syst. Signal Process. 28, 608–621 (2012)CrossRef
33.
Lingras, P., Yao, Y.Y.: Time complexity of rough clustering: GAs versus K-means. Rough Sets and Current Trends in Computing Proceedings. Book Series. Lecture Notes in Artificial Intelligence 2475, 263–270 (2002)MATH
34.
Lingras, P., Yan, R., West, C.: Comparison of conventional and rough K-means clustering. Rough Sets. Fuzzy Sets, Data Mining and Granular Computing. Book Series. Lecture Notes in Artificial Intelligence 2639, 130–137 (2003)MATH
35.
Lingras, P., West, C.: Interval set clustering of web users with rough K-means. J. Intell. Inf. Syst. 23(1), 5–16 (2004)MATHCrossRef
36.
Liu, D., Li, T., Ruan, D.: Probabilistic model criteria with decision-theoretic rough sets. Inf. Sci. 181(17), 3709–3722 (2011)MathSciNetCrossRef
37.
Liu, D., Yao, Y.Y., Li, T.: Three-way investment decisions with decision-theoretic rough sets. Int. J. Comput. Intell. Syst. 4(1), 66–74 (2011)CrossRef
38.
Liu, D., Yao, Y.Y., Li, T.: A Multiple-category classification approach with decision-theoretic rough sets. Fundamenta Informaticae 115(2–3), 173–188 (2012)MathSciNetMATH
39.
Maji, P., Pal, S.K.: Rough fuzzy c-medoids algorithm and selection of bio-basis for amino acid sequence analysis. IEEE Trans. Knowl. Data Eng. 19(6), 859–872 (2007)CrossRef
40.
Maji, P., Pal, S.K.: Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 37(6), 1529–1540 (2007)
41.
Maji, P.K., Roy, A.R.: An application of soft sets in a decision making problem. Comput. Math. Appl. 44(8–9), 1077–1083 (2002)MathSciNetMATHCrossRef
42.
43.
Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough set model. Inf. Sci. 159(3–4), 255–272 (2004)MathSciNetMATHCrossRef
44.
45.
46.
Mingers, J., Leydesdorff, L.: A Review of theory and practice in scientometrics. Eur. J. Oper. Res. 246(1), 1–19 (2015)MATHCrossRef
47.
Mitra, S., Hayashi, Y.: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans. Neural Netw. 11(3), 748–768 (2000)CrossRef
48.
49.
Pawłak, Z.: Rough sets. Int. J. Parallel. Program. 11(5), 341–356 (1982)MATH
50.
Pawłak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Commun. ACM 38(11), 88–95 (1995)CrossRef
51.
Pawłak, Z.: Rough set approach to knowledge-based decision support. Eur. J. Oper. Res. 99(1), 48–57 (1997)MATHCrossRef
52.
Pawłak, Z.: Rough set theory and its applications to data analysis. Cybern. Syst. 29(7), 661–688 (1998)MATHCrossRef
53.
54.
55.
56.
57.
Pedrycz, W.: Allocation of information granularity in optimization and decision-making models: towards building the foundations of granular computing. Eur. J. Oper. Res. 232(1), 137–145 (2014)CrossRef
58.
Polkowski, L., Skowron, A.: Rough mereology: a new paradigm for approximate reasoning. Int. J. Approx. Reason. 15(4), 333–365 (1996)MathSciNetMATHCrossRef
59.
Qian, Y.H., Liang, J.Y., Pedrycz, W., Dang, C.: An efficient accelerator for attribute reduction from incomplete data in rough set framework. Pattern Recogn. 44(8), 1658–1670 (2011)MATHCrossRef
60.
Qian, Y.H., Liang, J.Y., Wu, W.Z., Dang, C.: Information granularity in fuzzy binary GrC model. IEEE Trans. Fuzzy Syst. 19(2), 253–264 (2011)CrossRef
61.
Qian, J., Lv, P., Yue, X.D., Liu, C.H., Jing, Z.J.: Hierarchical attribute reduction algorithms for big data using MapReduce. Knowledge-Based Syst. 73, 18–31 (2015)CrossRef
62.
Qian, Y.H., Zhang, H., Sang, Y., Liang, J.Y.: Multigranulation decision-theoretic rough sets. Int. J. Approx. Reason. 55(1), 225–237 (2014)MathSciNetMATHCrossRef
63.
Qian, J., Miao, D.Q., Zhang, Z.H., Li, W.: Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation. Int. J. Approx. Reason. 52(2), 212–230 (2011)MathSciNetMATHCrossRef
64.
65.
Salehi, S., Selamat, A., Fujita, H.: Systematic mapping study on granular computing. Knowledge-Based Syst. 80, 78–97 (2015)CrossRef
66.
Skowron, A., Stepaniuk, J., Swiniarski, R.: Modeling rough granular computing based on approximation spaces. Inf. Sci. 184(1), 20–43 (2012)MATHCrossRef
67.
Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. IEEE Trans. Knowl. Data Eng. 12(2), 331–336 (2000)CrossRef
68.
Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833–849 (2003)MATHCrossRef
69.
Wang, C.Z., He, Q., Chen, D.G., Hu, Q.H.: A novel method for attribute reduction of coveringdecision systems. Inf. Sci. 254, 181–196 (2014)MATHCrossRef
70.
Wang, X.Y., Yang, J., Teng, X.L., Xia, W.J., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28(4), 459–471 (2007)CrossRef
71.
72.
73.
Wu, W.Z., Zhang, W.X.: Constructive and axiomatic approaches of fuzzy approximation operators. Inf. Sci. 159(3–4), 233–254 (2004)MathSciNetMATHCrossRef
74.
Wu, W.Z., Leung, Y.: Theory and applications of granular labelled partitions in multi-scale decision tables. Inf. Sci. 181(18), 3878–3897 (2011)MATHCrossRef
75.
Yao, J.T.: A ten-year review of granular computing. Proceedings of IEEE International Conference on Granular Computing, Silicon Valley, USA 2–5, 734–739 (2007)
76.
Yao, J.T., Zhang, Y.: A scientometrics study of rough sets in three decades. In: Proceedings of Rough Set and Knowledge Technology (RSKT 2013). Lecture Notes in Computer Science, vol. 8171, pp. 28–40 (2013)
77.
Yao, J.T., Vasilakos, A.V., Pedrycz, W.: Granular computing: perspectives and challenges. IEEE Trans. Cybern. 43(6), 1977–1989 (2013)CrossRef
78.
Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximating concepts. Int. J. Man-Mach. Stud. 37(6), 793–809 (1992)CrossRef
79.
80.
Yao, Y.Y.: Relational interpretations of neighborhood operators and rough set approximation operators. Inf. Sci. 111(1), 239–259 (1998)MathSciNetMATHCrossRef
81.
82.
Yao, Y.Y.: Information granulation and rough set approximation. Int. J. Intell. Syst. 16(1), 87–104 (2001)MATHCrossRef
83.
84.
85.
Yao, Y.Y.: Two semantic issues in a probabilistic rough set model. Fundamenta Informaticae 108(3–4), 249–265 (2011)MathSciNetMATH
86.
87.
Yu, H., Liu, Z.G., Wang, G.Y.: An automatic method to determine the number of clusters using decision-theoretic rough set. Int. J. Approx. Reason. 55(1), 101–115 (2014)MathSciNetMATHCrossRef
88.
Zhan, J.M., Liu, Q., Davvaz, B.: A new rough set theory: rough soft hemirings. J. Intell. Fuzzy Syst. 28(4), 1687–1697 (2015)MathSciNetMATH
89.
Zhang, X.H., Dai, J.H., Yu, Y.C.: On the union and intersection operations of rough sets based on various approximation spaces. Inf. Sci. 292, 214–229 (2015)MathSciNetMATHCrossRef
90.
Zhang, J., Li, T., Ruan, D., Liu, D.: Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems. Int. J. Approx. Reason. 53(4), 620–635 (2012)MathSciNetMATHCrossRef
91.
Zhang, J., Li, T., Ruan, D., Gao, Z., Zhao, C.: A parallel method for computing rough set approximations. Inf. Sci. 194, 209–223 (2012)CrossRef
92.
93.
Zhao, X.R., Hu, B.Q.: Fuzzy and interval-valued fuzzy decision-theoretic rough set approaches based on fuzzy probability measure. Inf. Sci. 298, 534–554 (2015)MathSciNetMATHCrossRef
94.
95.
96.
97.
Zhu, W., Wang, S.: Matroidal approaches to generalized rough sets based on relations. Int. J. Mach. Learn. Cybern. 2(4), 273–279 (2011)CrossRef
98.
Metadaten
Titel
Recent Development of Rough Computing: A Scientometrics View
verfasst von
Jing Tao Yao
Adeniyi Onasanya
Copyright-Jahr
2017
Buch
Thriving Rough Sets

Print ISBN: 978-3-319-54965-1

Electronic ISBN: 978-3-319-54966-8

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-54966-8_3

Premium Partner

    Bildnachweise