A projection based learning meta-cognitive RBF network classifier for effective diagnosis of parkinson's disease

Authors:
G. Sateesh Babu;S. Suresh;K. Uma Sangumathi;H. J. Kim
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;CIST, Korea University, Seoul, Korea
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

Citing 9
Cited 3

A sequential multi-category classifier using radial basis function networks

Neurocomputing
Risk-sensitive loss functions for sparse multi-category classification problems

Information Sciences: an International Journal
A comparison of multiple classification methods for diagnosis of Parkinson disease

Expert Systems with Applications: An International Journal
A sequential learning algorithm for self-adaptive resource allocation network classifier

Neurocomputing
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
A parallel neural network approach to prediction of Parkinson's Disease

Expert Systems with Applications: An International Journal
Meta-cognitive Neural Network for classification problems in a sequential learning framework

Neurocomputing
Metacognitive learning in a fully complex-valued radial basis function neural network

Neural Computation
A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN

IEEE Transactions on Neural Networks

Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems

Applied Soft Computing
Parkinson's disease prediction using gene expression - A projection based learning meta-cognitive neural classifier approach

Expert Systems with Applications: An International Journal
A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson's disease

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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.