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FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

  • Kamran KowsariEmail author
  • Nima Bari
  • Roman Vichr
  • Farhad A. Goodarzi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)

Abstract

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.

Keywords

Fuzzy logic Supervised learning Binary feature Learning algorithms Big data Classification task 

References

  1. 1.
    Brazdil, P., Carrier, C.G., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer (2008)Google Scholar
  2. 2.
    Fatehi, M., Asadi, H.H.: Application of semi-supervised fuzzy c-means method in clustering multivariate geochemical data, a case study from the dalli cu-au porphyry deposit in central iran. Ore Geol. Rev. 81, 245–255 (2017)CrossRefGoogle Scholar
  3. 3.
    Qiu, X., Ren, Y., Suganthan, P.N., Amaratunga, G.A.: Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft Comput. 54, 246–255 (2017)CrossRefGoogle Scholar
  4. 4.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kowsari, K., Brown, D.E., Heidarysafa, M., Jafari Meimandi, K., Gerber, M.S., Barnes, L.E.: Hdltex: hierarchical deep learning for text classification. In: IEEE International Conference on Machine Learning and Applications(ICMLA). IEEE (2017)Google Scholar
  6. 6.
    Ashfaq, R.A.R., Wang, X.-Z., Huang, J.Z., Abbas, H., He, Y.-L.: Fuzziness based semi-supervised learning approach for intrusion detection system. Inf. Sci. 378, 484–497 (2017)CrossRefGoogle Scholar
  7. 7.
    Jiang, X., Yi, Z., Lv, J.C.: Fuzzy SVM with a new fuzzy membership function. Neural Comput. Appl. 15(3–4), 268–276 (2006)CrossRefGoogle Scholar
  8. 8.
    Chen, S.-G., Wu, X.-J.: A new fuzzy twin support vector machine for pattern classification. Int. J. Mach. Learn. Cybern. 1–12 (2017)Google Scholar
  9. 9.
    Chen, C.P., Liu, Y.-J., Wen, G.-X.: Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems. IEEE Trans. Cybern. 44(5), 583–593 (2014)CrossRefGoogle Scholar
  10. 10.
    Sajja, P.S.: Computer aided development of fuzzy, neural and neuro-fuzzy systems. Empirical Research Press Ltd. (2017)Google Scholar
  11. 11.
    Lin, C., Lee, C.G.: Real-time supervised structure/parameter learning for fuzzy neural network. In: IEEE International Conference on Fuzzy Systems, pp. 1283–1291. IEEE (1992)Google Scholar
  12. 12.
    Thompson, T.M.: From Error-Correcting Codes Through Sphere Packings to Simple Groups, vol. 21. Cambridge University Press, Cambridge (1983)Google Scholar
  13. 13.
    West, J.: Commercializing open science: deep space communications as the lead market for shannon theory, 1960–73. J. Manage. Stud. 45(8), 1506–1532 (2008)CrossRefGoogle Scholar
  14. 14.
    Bahl, L., Chien, R.: On gilbert burst-error-correcting codes (corresp.). IEEE Trans. Inf. Theor. 15(3), 431–433 (1969)CrossRefGoogle Scholar
  15. 15.
    Yu, H., Jing, T., Chen, D., Berkovich, S.Y.: Golay code clustering for mobility behavior similarity classification in pocket switched networks. J. Commun. Comput. USA 4 (2012)Google Scholar
  16. 16.
    Rangare, U., Thakur, R.: A review on design and simulation of extended golay decoder. Int. J. Eng. Sci. 2058 (2016)Google Scholar
  17. 17.
    Berkovich, E.: Method of and system for searching a data dictionary with fault tolerant indexing, US Patent 7,168,025, 23 January 2007Google Scholar
  18. 18.
    Kowsari, K., Yammahi, M., Bari, N., Vichr, R., Alsaby, F., Berkovich, S.Y.: Construction of fuzzy find dictionary using golay coding transformation for searching applications. Int. J. Adv. Comput. Sci. Appl. 1(6), 81–87Google Scholar
  19. 19.
    Bari, N., Vichr, R., Kowsari, K., Berkovich, S.Y.: Novel metaknowledge-based processing technique for multimediata big data clustering challenges. In: 2015 IEEE International Conference on Multimedia Big Data (BigMM), pp. 204–207. IEEE (2015)Google Scholar
  20. 20.
    Kowsari, K.: Investigation of fuzzy find searching with golay code transformations, Master’s thesis. The George Washington University, Department of Computer Science (2014)Google Scholar
  21. 21.
    Bari, N., Vichr, R., Kowsari, K., Berkovich, S.: 23-bit metaknowledge template towards big data knowledge discovery and management. In: 2014 International Conference on Data Science and Advanced Analytics (DSAA), pp. 519–526. IEEE (2014)Google Scholar
  22. 22.
    Kamishima, T., Fujiki, J.: Clustering orders. In: International Conference on Discovery Science, pp. 194–207. Springer (2003)Google Scholar
  23. 23.
    Russo, M.: Genetic fuzzy learning. IEEE Trans. Evol. Comput. 4(3), 259–273 (2000)CrossRefGoogle Scholar
  24. 24.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)CrossRefGoogle Scholar
  25. 25.
    Qin, G., Huang, X., Chen, Y.: Nested one-to-one symmetric classification method on a fuzzy svm for moving vehicles. Symmetry 9(4), 48 (2017)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wieland, R., Mirschel, W.: Combining expert knowledge with machine learning on the basis of fuzzy training. Ecol. Inform. 38, 26–30 (2017)CrossRefGoogle Scholar
  27. 27.
    Prabu, M.J., Poongodi, P., Premkumar, K.: Fuzzy supervised online coactive neuro-fuzzy inference system-based rotor position control of brushless DC motor. IET Power Electron. 9(11), 2229–2239 (2016)CrossRefGoogle Scholar
  28. 28.
    Gama, J.: Knowledge Discovery from Data Streams. CRC Press (2010)Google Scholar
  29. 29.
    Learning from Data Streams. Springer (2007)Google Scholar
  30. 30.
    Höhle, U., Klement, E.P.: Non-classical logics and their applications to fuzzy subsets: a handbook of the mathematical foundations of fuzzy set theory, vol. 32. Springer (2012)Google Scholar
  31. 31.
    Zalta, E.N., etal.: Stanford Encyclopedia of Philosophy (2003)Google Scholar
  32. 32.
    Forrest, P.: The Identity of Indiscernibles (1996)Google Scholar
  33. 33.
    Logic, F.: Stanford Encyclopedia of Philosophy (2006)Google Scholar
  34. 34.
    Pinto, F., Soares, C., Mendes-Moreira, J.: A framework to decompose and develop meta features. In: Proceedings of the 2014 International Conference on Meta-learning and Algorithm Selection, vol. 1201. CEUR-WS. org, pp. 32–36 (2014)Google Scholar
  35. 35.
    Cargile, J.: The sorites paradox. Br. J. Philos. Sci. 20(3), 193–202 (1969)CrossRefGoogle Scholar
  36. 36.
    Malinowski, G.: Many-valued logic and its philosophy. In: Gabbay, D.M., Woods, J. (Eds.) The Many Valued and Nonmonotonic Turn in Logic, series: Handbook of the History of Logic North-Holland, vol. 8, pp. 13 – 94 (2007). http://www.sciencedirect.com/science/article/pii/S1874585707800045CrossRefGoogle Scholar
  37. 37.
    Dinis, B.: Old and new approaches to the sorites paradox, arXiv preprint arXiv:1704.00450 (2017)
  38. 38.
    Yammahi, M., Kowsari, K., Shen, C., Berkovich, S.: An efficient technique for searching very large files with fuzzy criteria using the pigeonhole principle. In: 2014 Fifth International Conference on Computing for Geospatial Research and Application (COM. Geo), pp. 82–86. IEEE (2014)Google Scholar
  39. 39.
    Evans, J.A., Foster, J.G.: Metaknowledge. Science 331(6018), 721–725 (2011)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Handzic, M.: Knowledge management: through the technology glass. World scientific, vol. 2 (2004)Google Scholar
  41. 41.
    Qazanfari, K., Youssef, A., Keane, K., Nelson, J.: A novel recommendation system to match college events and groups to students, arXiv:1709.08226v1 (2017)
  42. 42.
    Davis, R., Buchanan, B.G.: Meta-level knowledge. In: Rulebased Expert Systems, The MYCIN Experiments of the Stanford Heuristic Programming Project, BG Buchanan and Shortliffe, E. (Eds.). Addison-Wesley, Reading, pp. 507–530 (1984)Google Scholar
  43. 43.
    Vilalta, R., Giraud-Carrier, C.G., Brazdil, P., Soares, C.: Using meta-learning to support data mining. IJCSA 1(1), 31–45 (2004)zbMATHGoogle Scholar
  44. 44.
    Alassaf, M.H., Kowsari, K., Hahn, J.K.: Automatic, real time, unsupervised spatio-temporal 3D object detection using RGB-D cameras. In: 2015 19th International Conference on Information Visualisation (IV), pp. 444–449. IEEE (2015)Google Scholar
  45. 45.
    Kowsari, K., Alassaf, M.H.: Weighted unsupervised learning for 3D object detection. Int. J. Adv. Comput. Sci. Appl. 7(1), 584–593 (2016)Google Scholar
  46. 46.
    Qazanfari, K., Aslanzadeh, R., Rahmati, M.: An efficient evolutionary based method for image segmentation, arXiv preprint arXiv:1709.04393 (2017)
  47. 47.
    Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning. In: Chapelle, O. et al. (eds.) IEEE Transactions on Neural Networks [book reviews], vol. 20, no. 3, pp. 542–542 (2009)Google Scholar
  48. 48.
    Chapelle, O., Chi, M., Zien, A.: A continuation method for semi-supervised SVMS. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 185–192. ACM (2006)Google Scholar
  49. 49.
    Chapelle, O., Sindhwani, V., Keerthi, S.S.: Branch and bound for semi-supervised support vector machines. In: NIPS, pp. 217–224 (2006)Google Scholar
  50. 50.
    Choi, S.-S., Cha, S.-H., Tappert, C.C.: A survey of binary similarity and distance measures. J. Syst. Cybern. Inform. 8(1), 43–48 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kamran Kowsari
    • 1
    Email author
  • Nima Bari
    • 2
  • Roman Vichr
    • 3
  • Farhad A. Goodarzi
    • 4
  1. 1.Department of Computer ScienceUniversity of VirginiaCharlottesvilleUSA
  2. 2.Department of Computer ScienceThe George Washington UniversityWashingtonUSA
  3. 3.Data Mining and Surveillance and Metaknowledge DiscoveryFairfaxUSA
  4. 4.Department of Mechanical and Aerospace EngineeringThe George Washington UniversityWashingtonUSA

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