A Projection Based Learning Meta-cognitive RBF Network Classifier for Effective Diagnosis of Parkinson’s Disease

  • G. Sateesh Babu
  • S. Suresh
  • K. Uma Sangumathi
  • H. J. Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

In this paper, we proposed a ‘Projection Based Learning for Meta-cognitive Radial Basis Function Network (PBL-McRBFN)’ classifier for effective diagnosis of Parkinson’s disease. McRBFN is inspired by human meta-cognitive learning principles. McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, network parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. The experimental results on parkinson’s data sets based on vocal and gait features clearly highlight the superior performance of PBL-McRBFN classifier over results reported in the literature for detection of individual with or without PD.

Keywords

Extreme Learn Machine Hide Neuron Radial Basis Function Neural Network Radial Basis Function Network Cognitive Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • G. Sateesh Babu
    • 1
  • S. Suresh
    • 1
  • K. Uma Sangumathi
    • 1
  • H. J. Kim
    • 2
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.CISTKorea UniversitySeoulKorea

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