Self-adaptive Gesture Classifier Using Fuzzy Classifiers with Entropy Based Rule Pruning

  • Riidhei Malhotra
  • Ritesh Srivastava
  • Ajeet Kumar Bhartee
  • Mridula Verma
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)


Handwritten Gestures may vary from person to person. Moreover, they may vary for same person, if taken at different time and mood. Various rule-based automatic classifiers have been designed to recognize handwritten gestures. These classifiers generally include new rules in rule set for unseen inputs, and most of the times these new rules are distinguish from existing one. However, we get a huge set of rules which incurs problem of over fitting and rule base explosion. In this paper, we propose a self adaptive gesture fuzzy classifier which uses maximum entropy principle for preserving most promising rules and removing redundant rules from the rule set, based on interestingness. We present experimental results to demonstrate various comparisons from previous work and the reduction of error rates.


Handwritten Gesture Classifier Fuzzy Classifier Fuzzy Logic Entropy Rule Pruning 


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  1. 1.
    Almaksour, A., Anquetil, E., Quiniou, S., Cheriet, M.: Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition System. In: International Conference on Pattern Recognition (2010)Google Scholar
  2. 2.
    Aik, L.E., Jayakumar, Y.: A Study of Neuro-fuzzy System in Approximation-based Problems. Matematika 24(2), 113–130 (2008)Google Scholar
  3. 3.
    Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)CrossRefGoogle Scholar
  4. 4.
    Bedregal, B.C., Costa, A.C.R., Dimuro, G.P.: Fuzzy rule-based hand gesture recognition. In: Artificial Intelligence in Theory and Practice, pp. 285–294. Springer (2009)Google Scholar
  5. 5.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE TSMC 15(1), 116–132 (1985)MATHGoogle Scholar
  6. 6.
    Jang, J.-S.: Anfis: adaptive-network-based fuzzy inference system. IEEE Tr. on Systems, Man and Cybernetics (Part B) 23(3), 665–685 (1993)CrossRefGoogle Scholar
  7. 7.
    Angelov, P., Filev, D.: An approach to online identification of takagi-sugeno fuzzy models. IEEE Tr. Systems, Man, and Cybernetics 34(1), 484–498 (2004)CrossRefGoogle Scholar
  8. 8.
    de Barros, J.-C., Dexter, A.L.: On-line identification of computationally undemanding evolving fuzzy models. Fuzzy Sets and Systems 158(18), 1997–2012 (2007)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    McCallum, A., Pereira, F.: Maximum entropy Markov models for information extraction and segmentationGoogle Scholar
  10. 10.
    Zellnerr, A., Highfiled, R.: Calculation of Maximum Entropy Distributions and Approximation of Marginal Posterior Distributions. Journal of Econometric 37, 195–209 (1988)CrossRefGoogle Scholar
  11. 11.
    Berger, A.L., Pietra, S.A.D., Pietra, V.J.D.: A maximum entropy approach to natural language processingGoogle Scholar
  12. 12.
    Lazoand, V., Rathie, P.N.: On the Entropy of Continuous Probability Distributions. IEEE Trans. IT–24 (1978)Google Scholar
  13. 13.
    Jaynes: Papers on probability, statistics and statistical physics. Reidel Publishing Company, Dordrecht (1983)MATHGoogle Scholar
  14. 14.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Riidhei Malhotra
    • 1
  • Ritesh Srivastava
    • 2
  • Ajeet Kumar Bhartee
    • 2
  • Mridula Verma
    • 3
  1. 1.Department of Information TechnologyGalgotias College of Engineering & TechnologyGreater NoidaIndia
  2. 2.Department of Computer Science & EngineeringGalgotias College of Engineering & TechnologyGreater NoidaIndia
  3. 3.Indian Institute of Technology PatnaPatnaIndia

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