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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2017

09.06.2016 | Original Article

Cognitive concept learning from incomplete information

verfasst von: Yingxiu Zhao, Jinhai Li, Wenqi Liu, Weihua Xu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2017

Einloggen

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

search-config
loading …

Abstract

Cognitive concept learning is to learn concepts from a given clue by simulating human thought processes including perception, attention and thinking. In recent years, it has attracted much attention from the communities of formal concept analysis, cognitive computing and granular computing. However, the classical cognitive concept learning approaches are not suitable for incomplete information. Motivated by this problem, this study mainly focuses on cognitive concept learning from incomplete information. Specifically, we put forward a pair of approximate cognitive operators to derive concepts from incomplete information. Then, we propose an approximate cognitive computing system to perform the transformation between granular concepts as incomplete information is updated periodically. Moreover, cognitive processes are simulated based on three types of similarities. Finally, numerical experiments are conducted to evaluate the proposed cognitive concept learning methods.
Vorheriger Artikel Multi-scaled concept lattices based on neighborhood systems
Nächster Artikel Name identification and extraction with formal concept analysis

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
Weitere Produktempfehlungen anzeigen
Literatur
1.
2.
Aswani Kumar Ch (2012) Fuzzy clustering-based formal concept analysis for association rules mining. Appl Artif Intell 26(3):274–301
3.
Aswani Kumar Ch, Ishwarya MS, Loo CK (2015) Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biol Inspired Cognit Architect 12:20–33CrossRef
4.
Chen JK, Li JJ, Lin YJ, Lin GP, Ma ZM (2015) Relations of reduction between covering generalized rough sets and concept lattices. Inf Sci 304:16–27MathSciNetCrossRef
5.
Dias SM, Viera NJ (2013) Applying the JBOS reduction method for relevant knowledge extraction. Exp Syst Appl 40(5):1880–1887CrossRef
6.
Formica A (2008) Concept similarity in formal concept analysis: an information content approach. Knowl Based Syst 21:80–87CrossRef
7.
8.
Ganter B, Wille R (1999) Formal concept analysis: mathematical foundations. Springer, BerlinCrossRefMATH
9.
10.
Kang XP, Li DY, Wang SG, Qu KS (2012) Formal concept analysis based on fuzzy granularity base for different granulations. Fuzzy Sets Syst 203:33–48MathSciNetCrossRefMATH
11.
Li JH, Huang CC, Xu WH, Qian YH, Liu WQ (2015) Cognitive concept learning via granular computing for big data. In: Proceedings of the 2015 international conference on machine learning and cybernetics. Guangzhou, pp 289–294
12.
13.
Li JH, Mei CL, Lv YJ (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reas 54(1):149–165MathSciNetCrossRefMATH
14.
Li JH, Mei CL, Xu WH, Qian YH (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467MathSciNetCrossRef
15.
Li JH, Ren Y, Mei CL, Qian YH, Yang XB (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl Based Syst 91:152–164CrossRef
16.
Li LF, Zhang JK (2010) Attribute reduction in fuzzy concept lattices based on T implication. Knowl Based Syst 23(6):497–503CrossRef
17.
Lu SX, Wang XZ, Zhang GQ, Zhou X (2015) Effective algorithms of the Moore–Penrose inverse matrices for extreme learning machine. Intell Data Anal 19(4):743–760CrossRef
18.
Qi JJ, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl Based Syst 91:143–151CrossRef
19.
20.
21.
Pedrycz W (2013) Granular computing: analysis and design of intelligent systems. CRC Press/Francis Taylor, Boca RatonCrossRef
22.
Qian YH, Liang JY, Pedrycz W, Dang CY (2010) Positive approximation: an accelerator for attribute reduction in rough set theory. Artif Intell 174:597–618MathSciNetCrossRefMATH
23.
24.
Salehi S, Selamat A, Fujita H (2015) Systematic mapping study on granular computing. Knowl Based Syst 80:78–97CrossRef
25.
Shao MW, Leung Y, Wu WZ (2014) Rule acquisition and complexity reduction in formal decision contexts. Int J Approx Reas 55(1):259–274MathSciNetCrossRefMATH
26.
She YH (2014) On the rough consistency measures of logic theories and approximate reasoning in rough logic. Int J Approx Reas 55(1):486–499MathSciNetCrossRefMATH
27.
Singh PK, Aswani Kumar Ch (2014) Bipolar fuzzy graph representation of concept lattice. Inf Sci 288:437–448MathSciNetCrossRef
28.
Tan AH, Li JJ, Lin GP (2015) Connections between covering-based rough sets and concept lattices. Int J Approx Reas 56:43–58MathSciNetCrossRefMATH
29.
Wang XZ (2015) Uncertainty in learning from big data-editorial. J Intell Fuzzy Syst 28(5):2329–2330CrossRef
30.
Wang XZ, Ashfaq RAR, Fu AM (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185–1196MathSciNetCrossRef
31.
Wang XZ, Dong CR (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567MathSciNetCrossRef
32.
Wang XZ, Dong LC, Yan JH (2012) Maximum ambiguity based sample selection in fuzzy decision tree induction. IEEE Trans Knowl Data Eng 24(8):1491–1505CrossRef
33.
Wang XZ, Xing HJ, Li Y et al (2014) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23:1638–1654CrossRef
34.
35.
Wang LD, Liu XD (2008) A new model of evaluating concept similarity. Knowl Based Syst 21:842–846CrossRef
36.
Wang Y (2008) On concept algebra: a denotational mathematical structure for knowledge and sofrware modelling. Int J Cognit Inf Nat Intell 2(2):1–19CrossRef
37.
Wang Y (2009) On cognitive computing. Int J Softw Sci Comput Intell 1(3):1–15CrossRef
38.
Wei L, Wan Q (2016) Granular transformation and irreducible element judgment theory based on pictorial diagrams. IEEE Trans Cybernet 46(2):380–387CrossRef
39.
Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Reidel, Dordrecht, pp 445–470CrossRef
40.
Wu WZ, Leung Y, Mi JS (2009) Granular computing and knowledge reduction in formal contexts. IEEE Trans Knowl Data Eng 21(10):1461–1474CrossRef
41.
Wu WZ, Leung Y (2011) Theory and applications of granular labelled partitions in multi-scale decision tables. Inf Sci 181(18):3878–3897CrossRefMATH
42.
Xu WH, Li WT (2016) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybernet 46(2):366–379CrossRef
43.
Xu WH, Pang JZ, Luo SQ (2016) A novel cognitive system model and approach to transformation of information granules. Int J Approx Reas 55(3):853–866MathSciNetCrossRefMATH
44.
Yang XB, Qi Y, Yu HL, Song XN, Yang JY (2014) Updating multigranulation rough approximations with increasing of granular structures. Knowl Based Syst 64:59–69CrossRef
45.
Yao YY (2004) Concept lattices in rough set theory. Proceedings of 2004 annual meeting of the north american fuzzy information processing society. IEEE Computer Society, Washington, D.C., pp 796–801
46.
Yao YY(2204) A comparative study of formal concept analysis and rough set theory in data analysis. In: Proceedings of 4th international conference on rough sets and current trends in computing (RSCTC 2004), Uppsala, Sweden, pp 59–68
47.
Yao YY (2009) Interpreting concept learning in cognitive informatics and granular computing. IEEE Trans Syst Man Cybern Part B Cybern 39(4):855–866CrossRef
48.
Yao YY (2016) A triarchic theory of granular computing. Granul Comput 1(2):145–157CrossRef
49.
Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–117MathSciNetCrossRefMATH
50.
51.
Zhang WX, Xu WH (2007) Cognitive model based on granular computing. Chin J Eng Math 24(6):957–971MathSciNetMATH
Metadaten
Titel
Cognitive concept learning from incomplete information
verfasst von
Yingxiu Zhao
Jinhai Li
Wenqi Liu
Weihua Xu
Publikationsdatum
09.06.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0553-8

Weitere Artikel der Ausgabe 1/2017

International Journal of Machine Learning and Cybernetics 1/2017 Zur Ausgabe

Original Article

Constructing lattice based on irreducible concepts

Original Article

Concept acquisition approach of object-oriented concept lattices

Original Article

Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy

Original Article

n-Dimensional fuzzy hyperideals in semihyperrings

Original Article

Attributes reduction and rules acquisition in an lattice-valued information system with fuzzy decision

Original Article

Method for generating decision implication canonical basis based on true premises

Neuer Inhalt

    Bildnachweise