Machine Learning
The classification of human tremor signals using artificial neural network
Expert Systems with Applications: An International Journal
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
A vision-based analysis system for gait recognition in patients with Parkinson's disease
Expert Systems with Applications: An International Journal
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
A comparison of multiple classification methods for diagnosis of Parkinson disease
Expert Systems with Applications: An International Journal
Prediction of Parkinson's disease tremor onset using radial basis function neural networks
Expert Systems with Applications: An International Journal
Nonlinear Models Using Dirichlet Process Mixtures
The Journal of Machine Learning Research
An algebraic approach to Parkinson disease diagnosis
Expert Systems with Applications: An International Journal
Multiclass relevance vector machines: sparsity and accuracy
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
A parallel neural network approach to prediction of Parkinson's Disease
Expert Systems with Applications: An International Journal
A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
Expert Systems with Applications: An International Journal
A comparison of regression methods for remote tracking of Parkinson's disease progression
Expert Systems with Applications: An International Journal
Parkinson's disease classification using gait characteristics and wavelet-based feature extraction
Expert Systems with Applications: An International Journal
Advances in detecting parkinson's disease
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Expert Systems with Applications: An International Journal
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN
IEEE Transactions on Neural Networks
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this paper, we propose a gene expression based approach for the prediction of Parkinson's disease (PD) using 'projection based learning for meta-cognitive radial basis function network (PBL-McRBFN)'. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, a cognitive component and a meta-cognitive component. The cognitive component is a radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn of human learning principles efficiently. PBL-McRBFN classifier is used to predict PD using micro-array gene expression data obtained from ParkDB database. The performance of PBL-McRBFN classifier has been evaluated using Independent Component Analysis (ICA) reduced features sets from the complete genes and selected genes with two different significance levels. Further, the performance of PBL-McRBFN classifier is statistically compared with existing classifiers using one-way repeated ANOVA test. Further, it is also used in PD prediction using the standard vocal and gait PD data sets. In all these data sets, the performance of PBL-McRBFN is compared against existing results in the literature. Performance results clearly highlight the superior performance of our proposed approach.