Skip to main content
Erschienen in: International Journal of Machine Learning and Cybernetics 9/2019

28.10.2018 | Original Article

Attribute-oriented cognitive concept learning strategy: a multi-level method

verfasst von: Bingjiao Fan, Eric C. C. Tsang, Weihua Xu, Degang Chen, Wentao Li

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 9/2019

Einloggen

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

search-config
loading …

Abstract

Recently, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of cognitive concept learning. We establish a concept hierarchy structure based on the existing cognitive concept learning methods. However, none of these methods could obtain the following results: get the concept, recognize objects and distinguish between two different objects. In this paper, our focus is to construct an attribute-oriented multi-level cognitive concept learning method so as to improve and enhance the ability of cognitive concept learning. Firstly, the view point of human cognition is discussed from the multi-level approach, and then the mechanism of attribute-oriented cognitive concept learning is investigated. Through some defined special attributes, we propose a corresponding structure of attribute-oriented multi-level cognitive concept learning from an interdisciplinary viewpoint. It is a combination of philosophy and psychology of human cognition. Moreover, to make the presented attribute-oriented multi-level method easier to understand and apply in practice, an algorithm of cognitive concept learning is established. Furthermore, a case study about how to recognize the real-world animals is studied to use the proposed method and theory. Finally, in order to solve conceptual cognition problems, we perform an experimental evaluation on five data sets downloaded from the University of California-Irvine (UCI) databases. And then we provide a comparative analysis with the existing \(granular\ computing\ approach\ to\ two\)-\(way\ learning\) [44] and the three-\(way\ cognitive\ concept\ learning\ via\ multi\)-granularity [9]. We obtain more number of concepts than \(the\ two\)-\(way\ learning\ and\ the\ three\)-\(way\ cognitive\ concept\ learning\ approaches\), which shows the feasibility and effectiveness of our attribute-oriented multi-level cognitive learning method.

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!

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!

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!

Weitere Produktempfehlungen anzeigen
Literatur
2.
Zurück zum Zitat Bargiela A, Pedrycz W (2006) The roots of granular computing. In: IEEE international conference on granular computing, pp 806–809 Bargiela A, Pedrycz W (2006) The roots of granular computing. In: IEEE international conference on granular computing, pp 806–809
3.
Zurück zum Zitat Belohlávek R, Baets BD, Outrata J, Vychodil V (2009) Inducing decision trees via concept lattices. In: International conference on concept lattices and their applications, Cla 2007, Montpellier, France, October, DBLP, pp 455–467 Belohlávek R, Baets BD, Outrata J, Vychodil V (2009) Inducing decision trees via concept lattices. In: International conference on concept lattices and their applications, Cla 2007, Montpellier, France, October, DBLP, pp 455–467
4.
Zurück zum Zitat Düntsch I, Gediga G (2002) Modal-style operators in qualitative data analysis. In: IEEE international conference on data mining, 2002, ICDM 2003, pp 155–162 Düntsch I, Gediga G (2002) Modal-style operators in qualitative data analysis. In: IEEE international conference on data mining, 2002, ICDM 2003, pp 155–162
5.
Zurück zum Zitat Ganter B (1999) Formal concept analysis: mathematical foundations. Springer, New YorkCrossRefMATH Ganter B (1999) Formal concept analysis: mathematical foundations. Springer, New YorkCrossRefMATH
6.
Zurück zum Zitat Huang C, Li J, Mei C, Wu WZ (2017) Three-way concept learning based on cognitive operators: an information fusion viewpoint. Int J Approx Reason 83:218–242MathSciNetCrossRefMATH Huang C, Li J, Mei C, Wu WZ (2017) Three-way concept learning based on cognitive operators: an information fusion viewpoint. Int J Approx Reason 83:218–242MathSciNetCrossRefMATH
7.
Zurück zum Zitat Konecny J (2017) On attribute reduction in concept lattices: methods based on discernibility matrix are outperformed by basic clarification and reduction. Inf Sci 415–416:199–212CrossRef Konecny J (2017) On attribute reduction in concept lattices: methods based on discernibility matrix are outperformed by basic clarification and reduction. Inf Sci 415–416:199–212CrossRef
8.
Zurück zum Zitat Kumar CA, Ishwarya MS, Loo CK (2015) Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biol Inspir Cogn Archit 12:20–33 Kumar CA, Ishwarya MS, Loo CK (2015) Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biol Inspir Cogn Archit 12:20–33
9.
Zurück zum Zitat Li J, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263CrossRef Li J, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263CrossRef
11.
Zurück zum Zitat Li J, Mei C, Lv Y (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 54(1):149–165MathSciNetCrossRefMATH Li J, Mei C, Lv Y (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 54(1):149–165MathSciNetCrossRefMATH
12.
13.
Zurück zum Zitat Li J, Ren Y, Mei C, Qian Y, Yang X (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl Based Syst 91:152–164CrossRef Li J, Ren Y, Mei C, Qian Y, Yang X (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl Based Syst 91:152–164CrossRef
14.
Zurück zum Zitat Li W, Pedrycz W, Xue X, Xu W, Fan B (2018) Distance-based double-quantitative rough fuzzy sets with logic operations. Int J Approx Reason 101:206–233MathSciNetCrossRefMATH Li W, Pedrycz W, Xue X, Xu W, Fan B (2018) Distance-based double-quantitative rough fuzzy sets with logic operations. Int J Approx Reason 101:206–233MathSciNetCrossRefMATH
15.
Zurück zum Zitat Liu M, Shao M, Zhang W, Wu C (2007) Reduction method for concept lattices based on rough set theory and its application. Comput Math Appl 53(9):1390–1410MathSciNetCrossRefMATH Liu M, Shao M, Zhang W, Wu C (2007) Reduction method for concept lattices based on rough set theory and its application. Comput Math Appl 53(9):1390–1410MathSciNetCrossRefMATH
16.
Zurück zum Zitat Luksch P, Wille R (1991) A mathematical model for conceptual knowledge systems. Classification, data analysis, and knowledge organization. Springer, Berlin, pp 156–162CrossRefMATH Luksch P, Wille R (1991) A mathematical model for conceptual knowledge systems. Classification, data analysis, and knowledge organization. Springer, Berlin, pp 156–162CrossRefMATH
17.
Zurück zum Zitat Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R (2011) Cognitive computing. Commun ACM 54(8):62–71CrossRef Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R (2011) Cognitive computing. Commun ACM 54(8):62–71CrossRef
18.
Zurück zum Zitat Moreton E, Pater J, Pertsova K (2017) Phonological concept learning. Cogn Sci 41(1):4–69CrossRef Moreton E, Pater J, Pertsova K (2017) Phonological concept learning. Cogn Sci 41(1):4–69CrossRef
19.
Zurück zum Zitat Pedrycz W, Skowron A, Kreinovich V (2008) Handbook of granular computing. Wiley-Interscience, HobokenCrossRef Pedrycz W, Skowron A, Kreinovich V (2008) Handbook of granular computing. Wiley-Interscience, HobokenCrossRef
20.
Zurück zum Zitat Pei D, Mi JS (2011) Attribute reduction in decision formal context based on homomorphism. Int J Mach Learn Cybern 2(4):289–293CrossRef Pei D, Mi JS (2011) Attribute reduction in decision formal context based on homomorphism. Int J Mach Learn Cybern 2(4):289–293CrossRef
21.
Zurück zum Zitat Pinggera J (2015) Visualizing human behavior and cognition: the case of process modeling. In: International conference on business process management, Springer, Cham, pp 547–551 Pinggera J (2015) Visualizing human behavior and cognition: the case of process modeling. In: International conference on business process management, Springer, Cham, pp 547–551
22.
Zurück zum Zitat Qi J, Wei L, Yao Y (2014) Three-way formal concept analysis. In: International conference on rough sets and knowledge technology, Springer, Cham, pp 732–741 Qi J, Wei L, Yao Y (2014) Three-way formal concept analysis. In: International conference on rough sets and knowledge technology, Springer, Cham, pp 732–741
23.
Zurück zum Zitat Qi J, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl Based Syst 91:143–151CrossRef Qi J, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl Based Syst 91:143–151CrossRef
24.
Zurück zum Zitat Rodríguez-Jiménez JM, Cordero P, Enciso M, Mora A (2014) A generalized framework to consider positive and negative attributes in formal concept analysis. In: Bertet K, Rudolph S (eds) Proceedings of the eleventh international conference on concept lattices and their applications, CLA 2014. Pavol Jozef Šafárik University in Košice, Slovakia, pp 267–279 Rodríguez-Jiménez JM, Cordero P, Enciso M, Mora A (2014) A generalized framework to consider positive and negative attributes in formal concept analysis. In: Bertet K, Rudolph S (eds) Proceedings of the eleventh international conference on concept lattices and their applications, CLA 2014. Pavol Jozef Šafárik University in Košice, Slovakia, pp 267–279
25.
Zurück zum Zitat Rodríguez-Jiménez JM, Cordero P, Enciso M, Rudolph S (2016) Concept lattices with negative information: a characterization theorem. Inf Sci 369:51–62MathSciNetCrossRef Rodríguez-Jiménez JM, Cordero P, Enciso M, Rudolph S (2016) Concept lattices with negative information: a characterization theorem. Inf Sci 369:51–62MathSciNetCrossRef
26.
Zurück zum Zitat Shao M, Yang H (2013) Two kinds of multi-level formal concepts and its application for sets approximations. Int J Mach Learn Cybern 4(6):621–630CrossRef Shao M, Yang H (2013) Two kinds of multi-level formal concepts and its application for sets approximations. Int J Mach Learn Cybern 4(6):621–630CrossRef
27.
Zurück zum Zitat Shivhare R, Cherukuri AK (2017) Three-way conceptual approach for cognitive memory functionalities. Int J Mach Learn Cybern 8(1):21–34CrossRef Shivhare R, Cherukuri AK (2017) Three-way conceptual approach for cognitive memory functionalities. Int J Mach Learn Cybern 8(1):21–34CrossRef
28.
Zurück zum Zitat Shivhare R, Cherukuri AK, Li J (2017) Establishment of cognitive relations based on cognitive informatics. Cogn Comput 9(5):721–729CrossRef Shivhare R, Cherukuri AK, Li J (2017) Establishment of cognitive relations based on cognitive informatics. Cogn Comput 9(5):721–729CrossRef
29.
Zurück zum Zitat Singh PK, Cherukuri AK, Li J (2017) Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy. Int J Mach Learn Cybern 8(1):179–189CrossRef Singh PK, Cherukuri AK, Li J (2017) Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy. Int J Mach Learn Cybern 8(1):179–189CrossRef
30.
Zurück zum Zitat Vormbrock B (2005) Complete subalgebras of semiconcept algebras and protoconcept algebras. In: International conference on formal concept analysis, Berlin, Heidelberg, pp 329–343 Vormbrock B (2005) Complete subalgebras of semiconcept algebras and protoconcept algebras. In: International conference on formal concept analysis, Berlin, Heidelberg, pp 329–343
31.
Zurück zum Zitat Wang H, Zhang WX (2008) Approaches to knowledge reduction in generalized consistent decision formal context. Math Comput Model 48(11–12):1677–1684MathSciNetCrossRefMATH Wang H, Zhang WX (2008) Approaches to knowledge reduction in generalized consistent decision formal context. Math Comput Model 48(11–12):1677–1684MathSciNetCrossRefMATH
32.
Zurück zum Zitat Wang R, Wang XZ, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEEE Trans Fuzzy Syst 25(6):1460–1475CrossRef Wang R, Wang XZ, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEEE Trans Fuzzy Syst 25(6):1460–1475CrossRef
33.
Zurück zum Zitat Wang XZ, Wang R, Xu C (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715MathSciNetCrossRef Wang XZ, Wang R, Xu C (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715MathSciNetCrossRef
34.
Zurück zum Zitat Wang XZ, He YL, Wang DD (2014) Non-naive Bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern 44(1):21–39CrossRef Wang XZ, He YL, Wang DD (2014) Non-naive Bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern 44(1):21–39CrossRef
35.
Zurück zum Zitat Wang XZ, Wang R, Feng HM, Wang HC (2014) A new approach to classifier fusion based on upper integral. IEEE Trans Cybern 44(5):620–635MathSciNetCrossRef Wang XZ, Wang R, Feng HM, Wang HC (2014) A new approach to classifier fusion based on upper integral. IEEE Trans Cybern 44(5):620–635MathSciNetCrossRef
36.
Zurück zum Zitat Wang X, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654CrossRef Wang X, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654CrossRef
37.
Zurück zum Zitat Wang Y (2008) On concept algebra: a denotational mathematical structure for knowledge and software modeling. Int J Cogn Inform Nat Intell 2(2):1–19CrossRef Wang Y (2008) On concept algebra: a denotational mathematical structure for knowledge and software modeling. Int J Cogn Inform Nat Intell 2(2):1–19CrossRef
38.
Zurück zum Zitat Wang Y (2009) On cognitive computing. Int J Softw Sci Comput Intell 1(3):1–15CrossRef Wang Y (2009) On cognitive computing. Int J Softw Sci Comput Intell 1(3):1–15CrossRef
39.
Zurück zum Zitat Wang Y, Chiew V (2010) On the cognitive process of human problem solving. Cogn Syst Res 11(1):81–92CrossRef Wang Y, Chiew V (2010) On the cognitive process of human problem solving. Cogn Syst Res 11(1):81–92CrossRef
40.
Zurück zum Zitat Wang Y, Zadeh LA, Yao Y (2012) On the system algebra foundations for granular computing. In: Software and intelligent sciences: new transdisciplinary findings, \(|G|\) Global, pp 98–121 Wang Y, Zadeh LA, Yao Y (2012) On the system algebra foundations for granular computing. In: Software and intelligent sciences: new transdisciplinary findings, \(|G|\) Global, pp 98–121
41.
Zurück zum Zitat Wille R (1992) Concept lattices and conceptual knowledge systems. Comput Math Appl 23(6–9):493–515CrossRefMATH Wille R (1992) Concept lattices and conceptual knowledge systems. Comput Math Appl 23(6–9):493–515CrossRefMATH
42.
Zurück zum Zitat Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. Ordered sets. Springer, Dordrecht, pp 445–470MATH Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. Ordered sets. Springer, Dordrecht, pp 445–470MATH
43.
Zurück zum Zitat Wu WZ, Leung Y, Mi JS (2009) Granular computing and knowledge reduction in formal contexts. IEEE Trans Knowl Data Eng 21(10):1461–1474CrossRef Wu WZ, Leung Y, Mi JS (2009) Granular computing and knowledge reduction in formal contexts. IEEE Trans Knowl Data Eng 21(10):1461–1474CrossRef
44.
Zurück zum Zitat Xu W, Li W (2016) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybern 46(2):366–379MathSciNetCrossRef Xu W, Li W (2016) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybern 46(2):366–379MathSciNetCrossRef
45.
Zurück zum Zitat Xu W, Pang J, Luo S (2014) A novel cognitive system model and approach to transformation of information granules. Int J Approx Reason 55(3):853–866MathSciNetCrossRefMATH Xu W, Pang J, Luo S (2014) A novel cognitive system model and approach to transformation of information granules. Int J Approx Reason 55(3):853–866MathSciNetCrossRefMATH
46.
Zurück zum Zitat Yao Y (2004) A comparative study of formal concept analysis and rough set theory in data analysis. In: International conference on rough sets and current trends in computing, Springer, Berlin, Heidelberg, pp 59–68 Yao Y (2004) A comparative study of formal concept analysis and rough set theory in data analysis. In: International conference on rough sets and current trends in computing, Springer, Berlin, Heidelberg, pp 59–68
47.
Zurück zum Zitat Yao Y (2017) Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn Cybern 8(1):3–20CrossRef Yao Y (2017) Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn Cybern 8(1):3–20CrossRef
48.
Zurück zum Zitat Yao Y (2009) Interpreting concept learning in cognitive informatics and granular computing. IEEE Trans Syst Man Cybern Part B (Cybern) 39(4):855–866CrossRef Yao Y (2009) Interpreting concept learning in cognitive informatics and granular computing. IEEE Trans Syst Man Cybern Part B (Cybern) 39(4):855–866CrossRef
49.
Zurück zum Zitat Yao YY (2004) Concept lattices in rough set theory. Fuzzy Information, 2004, Processing NAFIPS’04. In: IEEE annual meeting of the IEEE, vol 2, pp 796–801 Yao YY (2004) Concept lattices in rough set theory. Fuzzy Information, 2004, Processing NAFIPS’04. In: IEEE annual meeting of the IEEE, vol 2, pp 796–801
50.
Zurück zum Zitat Yao YY (2001) On modeling data mining with granular computing. In: Computer software and applications conference, COMPSAC, 2001 25th annual international. IEEE, pp 638–643 Yao YY (2001) On modeling data mining with granular computing. In: Computer software and applications conference, COMPSAC, 2001 25th annual international. IEEE, pp 638–643
51.
Zurück zum Zitat Zhao Y, Li J, Liu W, Xu W (2017) Cognitive concept learning from incomplete information. Int J Mach Learn Cybern 8(1):159–170CrossRef Zhao Y, Li J, Liu W, Xu W (2017) Cognitive concept learning from incomplete information. Int J Mach Learn Cybern 8(1):159–170CrossRef
Metadaten
Titel
Attribute-oriented cognitive concept learning strategy: a multi-level method
verfasst von
Bingjiao Fan
Eric C. C. Tsang
Weihua Xu
Degang Chen
Wentao Li
Publikationsdatum
28.10.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 9/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0879-5

Weitere Artikel der Ausgabe 9/2019

International Journal of Machine Learning and Cybernetics 9/2019 Zur Ausgabe

Neuer Inhalt