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)

Abstract

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.

Keywords

Handwritten Gesture Classifier Fuzzy Classifier Fuzzy Logic Entropy Rule Pruning 

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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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