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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings
Pages611-620
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 2012
Event9th International Symposium on Neural Networks, ISNN 2012 - Shenyang, China
Duration: 2012 Jul 112012 Jul 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7368 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Symposium on Neural Networks, ISNN 2012
CountryChina
CityShenyang
Period12/7/1112/7/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Sateesh Babu, G., Suresh, S., Uma Sangumathi, K., & Kim, H. J. (2012). A projection based learning meta-cognitive RBF network classifier for effective diagnosis of Parkinson's disease. In Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings (PART 2 ed., pp. 611-620). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7368 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-31362-2_67