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Three-way conceptual approach for cognitive memory functionalities

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Abstract

Human brain represents the information and stores it as memory. They are stored in different parts of the brain and are linked together by associations. When a cue is provided, the memory is recalled through association. Encoding of the real world information is in the form of object-attribute relation. It is possible to perform both positive recall (object having the attribute and attribute shared by object) and negative recalls (object not having the attribute and attribute not shared by object) from memory. It is evident from literature that the formal concept analysis (FCA) based on bidirectional associative memory (BAM) performs only positive recall from memory. In this paper, FCA based on BAM is extended to three-way formal concept analysis (3WFCA) to achieve a more precise recall. In this extended model, both positive recall and negative recall are performed. In order to achieve this objective, an extra operator namely negative operator is added. The proposed model is validated with an experiment on real world scenario. We also presented the connection of the proposal with long term potentiation (LTP) and Hippocampus of the human brain.

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References

  1. Wang Y, Wang Y, Patel S, Patel D (2003) A layered reference model of the brain (LRMB). IEEE Trans Syst Man Cybern Part C Appl Rev 36(2):24–133

    Google Scholar 

  2. Berger JO (2013) Statistical decision theory: foundations, concepts, and methods. Springer Science & Business Media, Berlin

    Google Scholar 

  3. Reid Hastie (2001) Problems for judgment and decision making. Annu Rev Psychol 52(1):653–683

    Article  MathSciNet  Google Scholar 

  4. Yiyu Y (2012) An outline of a theory of three-way decisions. International Conference on Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, pp 1–17

  5. Huaxiong Li and Xianzhong Zhou (2011) “Risk decision making based on decision-theoretic rough set: a three-way view decision model. Int J Comput Intell Syst 4(1):1–11

    Article  Google Scholar 

  6. Yiyu Y (2016) Three-way decisions and cognitive computing. Cogn Comput. doi:10.1007/s12559-016-93975

    Google Scholar 

  7. Wang XZ et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654

    Article  Google Scholar 

  8. Wang XZ, Huang JZ (2015) Learning from big data with uncertainty—editorial. J Intell Fuzzy Syst 28(5):2329–2330

    Article  MathSciNet  Google Scholar 

  9. Ashfaq RAR et al (2016) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci. doi:10.1016/j.ins.2016.04.019

    Google Scholar 

  10. Wang Yingxu (2008) On concept algebra: a denotational mathematical structure for knowledge and software modeling. Int J Cogn Inform Nat Intell (IJCINI) 2(2):1–19

    Article  Google Scholar 

  11. Radhika Shivhare, Aswani Kumar Ch (2016) On the cognitive process of abstraction. International Conference on Communication Networks 89: 243–252

  12. Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. Ordered sets. Springer, The Netherlands, pp 445–470

    Chapter  Google Scholar 

  13. Li J, Mei C, Xu W, Qian Y (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467

    Article  MathSciNet  Google Scholar 

  14. Li Jinhai, Mei Changlin, Lv Yuejin (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 54(1):149–165

    Article  MathSciNet  MATH  Google Scholar 

  15. Wang Yingxu (2008) On system algebra: a denotational mathematical structure for abstract system modeling. Int J Cogn Inform Nat Intell (IJCINI) 2(2):20–43

    Article  Google Scholar 

  16. Jianjun Qi, Ling Wei, Yiyu Y (2014) Three-way formal concept analysis. International Conference on rough sets and knowledge technology, Springer International Publishing, New york, pp 732-741

  17. Kosko Bart (1988) Bidirectional associative memories. IEEE Trans Syst Man Cybern 18(1):49–60

    Article  MathSciNet  Google Scholar 

  18. Stumme Gerd (2009) Formal concept analysis. Handbook on ontologies, Springer, pp 177–199

    Google Scholar 

  19. Zhou Bing, Yao Yiyu, Luo Jigang (2014) Cost-sensitive three-way email spam filtering. J Intell Inform Syst 42(1):19–45

    Article  Google Scholar 

  20. Kumar CA, Ishwarya MS, Loo Chu Kiong (2015) Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biol Inspired Cogn Archit 12:20–33

    Google Scholar 

  21. Qi Jianjun, Qian Ting, Wei Ling (2016) The connections between three-way and classical concept lattices. Knowl Based Syst 91:143–151

    Article  Google Scholar 

  22. Kumar CA, Ishwarya MS, Loo Chu Kiong (2015) Computational Intelligence in Information Systems. Modeling associative memories using formal concept analysis. Springer International Publishing, New York, pp 109–118

    Google Scholar 

  23. Bělohlávek R (2000) Representation of concept lattices by bidirectional associative memories. Neural Comput 12(10): 2279–2290

    Article  Google Scholar 

  24. Zarate LE, Mariano Dias S, Junho Song MA (2008) FCANN: a new approch for extraction and representation of knowledge from ANN trained via formal concept analysis. Neurocomputing 71(13):2670–2684

    Article  Google Scholar 

  25. Bělohlávek R (2000) Fuzzy logical bidirectional associative memory. Inf Sci 128(1):91–103

    Article  MathSciNet  MATH  Google Scholar 

  26. Singh PK, Kumar CA, Jinhai L (2016) Knowledge representation using interval-valued fuzzy formal concept lattice. Soft Comput 20(4):1485–1502

    Article  Google Scholar 

  27. Annapurna J, Cherukuri AK (2013) Exploring attributes with domain knowledge in formal concept analysis. CIT J Comput Inform Technol 21(1):109–123

    Article  Google Scholar 

  28. Chen Y, Yiyu Y (2005) Formal concept analysis based on hierarchical class analysis. In: Fourth IEEE conference on cognitive informatics (ICCI), pp 285–292

  29. Aswani Kumar C, Radvansky M, Annapurna J (2012) Analysis of a vector space model, latent semantic indexing and formal concept analysis for information retrieval. Cybern Inform Technol 12(1):34–38

    Google Scholar 

  30. Muthukrishnan AK (2006) Information retrieval using concept lattices. Dissertation, University of Cincinnati

  31. Acevedo ME, Yáñez-Márquez C, Acevedo MA (2010) Associative models for storing and retrieving concept lattices. Math Prob Eng 2010:356029. doi:10.1155/2010/356029

    Article  MathSciNet  MATH  Google Scholar 

  32. Kitto K, Bruza P, Gabora L (2012) A quantum informtion retrieval approch to memory. In: IEEE International joint conference on neural networks (IJCNN), pp 1–8

  33. Aswani Kumar Ch, Dias SM, Vieira NJ (2015) Knowledge reduction in formal contexts using non-negative matrix factorization. Math Comput Simul 109:46–63

    Article  MathSciNet  Google Scholar 

  34. Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37(3):2696–2704

    Article  Google Scholar 

  35. Aswani Kumar Ch (2011) Mining association rules using non-negative matrix factorization and formal concept analysis. In: Computer networks and intelligent computing. Springer, Berlin, Heidelberg, pp 31–39

  36. Deng B, Yajun Du (2007) Research on matching between user queries and Web pages based on BAM. Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing(SNPD), Eighth ACIS International Conference on. Vol 3, pp 81–85

  37. Deng X, Yiyu Y (2014) Decision-theoretic three-way approximations of fuzzy sets. Inf Sci 279:702–715

    Article  MathSciNet  Google Scholar 

  38. Hu BQ (2014) Three-way decisions space and three-way decisions. Inf Sci 281:21–52

    Article  MathSciNet  Google Scholar 

  39. Liu D, Li Tianrui, Li Huaxiong (2012) A multiple-category classification approach with decision-theoretic rough sets. Fundam Inform 115(2–3):173–188

    MathSciNet  MATH  Google Scholar 

  40. Yiyu Y (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180(3):341–353

    Article  MathSciNet  Google Scholar 

  41. Yiyu Y (2009) Three-way decision: an interpretation of rules in rough set theory. International Conference on rough sets and knowledge technology, Springer, Berlin, pp 642–649

  42. Li Jinhai et al (2016) Three-way cognitive concept learning via multi-granularity. Inf Sci. doi:10.1016/j.ins.2016.04.051

    Google Scholar 

  43. Zhao Y, Li J, Liu W, Xu W (2016) Cognitive concept learning from incomplete information. Int J Mach Learn Cyber 1–12. doi:10.1007/s13042-016-0553-8

  44. Wang XZ et al (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185–1196

    Article  MathSciNet  Google Scholar 

  45. Yu-Lin HE, Wang XZ, Huang JZ (2016) Fuzzy nonlinear regression analysis using a random weight network. Inf Sci 364–365:222–240

    Google Scholar 

  46. Yu-Lin HE et al (2015) OWA operator based link prediction ensemble for social network. Expert Syst Appl 42(1):21–50

    Article  Google Scholar 

  47. Howes MB, O’Shea G (2013) Human memory: a constructivist view. Elsevier

  48. Rumbell T (2013) Self organisation and hierarchical concept representation in networks of spiking neurons. Dissertation, Plymouth University

  49. Weihua Xu, Li Wentao (2016) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE trans cybern 46(2):366–379

    Article  Google Scholar 

  50. King JA et al (2004) The hippocampal role in spatial memory and the familiarity-recollection distinction: a case study.”. Neuropsychology 18(3):405

    Article  Google Scholar 

  51. Phelps EA (2004) Human emotion and memory: interactions of the amygdala and hippocampal complex. Curr Opin Neurobiol 14(2):198–202

    Article  Google Scholar 

Download references

Acknowledgments

Authors sincerely acknowledge the financial support from the Department of Science & Technology, Govt. of India under the Grant SR/CSRI/118/2014. Authors sincerely thank the anonymous reviewers for their most useful suggestions which have helped us a lot in improving this article.

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Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, 632014, India

    Radhika Shivhare & Aswani Kumar Cherukuri

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  1. Radhika Shivhare
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Correspondence to Aswani Kumar Cherukuri.

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Shivhare, R., Cherukuri, A.K. Three-way conceptual approach for cognitive memory functionalities. Int. J. Mach. Learn. & Cyber. 8, 21–34 (2017). https://doi.org/10.1007/s13042-016-0593-0

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