Meta-cognitive sequential learning in RBF network for diagnosis of neurodegenerative diseases

This research work focuses on the development of meta-cognitive sequential learning algorithms in Radial Basis Function (RBF) network classifiers, and their application to the early diagnosis of neurodegenerative diseases. The important issues in existing sequential learning algorithms are proper selection of training samples, finding minimal network structure and selection of an appropriate learning strategy. In addition, the random sequence of sample arrival influences the performance significantly. It has been reported in human learning that best learning strategies employ meta-cognition (meta-cognition means cognition about cognition) to address fundamental problems of what-to-learn, when-to-learn and how-to-learn. This thesis develops such meta-cognitive sequential learning algorithms in RBF network for classification problems. We call a RBF network employing meta-cognitive algorithm as `meta-cognitive RBF network' (McRBFN). McRBFN is developed based on Nelson and Narens model of meta-cognition for human learning. Accordingly, McRBFN has two components, namely cognitive and meta-cognitive components. A RBF network with evolving structure is the cognitive component and a self-regulatory learning mechanism is its meta-cognitive component. The meta-cognitive component controls the learning of cognitive component by choosing suitable learning strategies for each sample. When a new sample is presented, the meta-cognitive component either deletes the sample or learns the sample or reserves the sample for future use. Learning includes adding a new neuron or updating the parameters of the existing neurons using an extended Kalman filter (EKF). The McRBFN using EKF for parameter updates are referred as `EKF-McRBFN'. EKF-McRBFN uses computationally intensive EKF based parameter update and does not utilize the past knowledge stored in the network. Therefore, an efficient Projection Based Learning (PBL) algorithm for McRBFN referred as PBL-McRBFN has been developed. When a neuron is added to the cognitive component, the Gaussian parameters are determined based on the current sample and the output weights are estimated using the PBL algorithm. When a new neuron is added, existing neurons in the cognitive component will be used as pseudo-samples in PBL. There-by, the proposed algorithm exploits the knowledge stored in the network for proper initialization. The performance of EKF-McRBFN and PBL-McRBFN has been evaluated using a number of benchmark classification problems. The statistical performance comparisons on multiple data sets clearly indicate the superior performance of the proposed PBL-McRBFN and EKF-McRBFN over existing popular classifiers. Experimental results also show that PBL-McRBFN performance is better than EKF-McRBFN classifier. Another significant contribution of this thesis is in early diagnosis of neurodegenerative diseases. In this thesis, we employed PBL-McRBFN to early diagnosis of Alzheimer's disease (AD) and Parkinson's disease (PD). The early diagnosis of AD problem from Magnetic Resonance Imaging (MRI) scans is formed as a binary classification problem. The performance of the PBL-McRBFN classifier has been evaluated on two well-known open access Open Access Series of Imaging Studies (OASIS) and Alzheimer's disease Neuroimaging Initiative (ADNI) data sets. Morphometric features are extracted from MRI scans using Voxel-Based Morphometry (VBM). The study results clearly show that the PBL-RBFN classifier produces a better generalization performance compared to the state-of-the-art AD detection results. Also, generalization conducted on ADNI data set with PBL-McRBFN classifier trained on OASIS data set shows that the proposed PBL-McRBFN can also achieve significant results on the unseen data set. Finally, PBL-McRBFN-RFE feature selection approach has been proposed to detect imaging biomarkers responsible for AD for different age groups and for both genders using OASIS data set. The early diagnosis of PD problem is also formed as a binary classification problem. PBL-McRBFN classifier is used to predict PD using microarray gene expression data. Next, PBL-McRBFN classifier is used to predict PD from MRI scans. Further, imaging biomarkers responsible for PD are detected with the proposed PBL-McRBFN-RFE approach based on MRI scans. For completeness, PBL-McRBFN classifier is also used to detect PD from vocal and gait features. From the performance evaluation study, it is evident that the generalization performance of proposed PBL-McRBFN classifier is better than the state-of-the-art PD detection results.DOCTOR OF PHILOSOPHY (SCE

Meta-cognitive neural network for classification problems in a sequential learning framework

In this paper, we propose a sequential learning algorithm for a neural network classifier based on human meta-cognitive learning principles. The network, referred to as Meta-cognitive Neural Network (McNN). McNN has two components, namely the cognitive component and the meta-cognitive component. A radial basis function network is the fundamental building block of the cognitive component. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. When a sample is presented at the cognitive component of McNN, the meta-cognitive component chooses the best learning strategy for the sample using estimated class label, maximum hinge error, confidence of classifier and class-wise significance. Also sample overlapping conditions are considered in growth strategy for proper initialization of new hidden neurons. The performance of McNN classifier is evaluated using a set of benchmark classification problems from the UCI machine learning repository and two practical problems, viz., the acoustic emission for signal classification and a mammogram data set for cancer classification. The statistical comparison clearly indicates the superior performance of McNN over reported results in the literature

A projection based learning in Meta-cognitive Radial Basis Function Network for classification problems

In this paper, we propose a `Meta-cognitive Radial Basis Function Network (McRBFN)' and its `Projection Based Learning (PBL)' algorithm for classification problems. McRBFN emulates human-like meta-cognitive learning principles. As each sample is presented to the network, 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. McRBFN addresses the what-to-learn by choosing samples to participate in the learning process, also deleting samples with information similar to that already learnt by the network. A few samples that satisfy neither of these criteria are pushed to the rear end of the training data stack to be used in future, thereby satisfying the when-to-learn. The how-to-learn component of meta-cognition is addressed by using the participating samples to either add a neuron or update the output weights. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, its 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 use of human meta-cognitive principles ensures efficient learning. Moreover, as samples with similar information are deleted, overtraining is avoided. The PBL algorithm helps to reduce the computational effort used in training. The performance of the PBL-McRBFN classifier is evaluated using a set of benchmark classification problems from the UCI machine learning repository. The performance evaluation study on these problems clearly indicates the superior performance of PBL-McRBFN classifier over results reported in the literature

A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease

In this paper, we present a novel approach for the identification of critical brain regions responsible for Parkinson’s disease (PD) based on magnetic resonance images (MRI) using meta-cognitive radial basis function network (McRBFN) classifier with Recursive Feature Elimination (RFE). The McRBFN classifier uses voxel based morphometric (VBM) features extracted from MRI and employs a projection based learning (PBL) algorithm. The meta-cognitive learning present in PBL-McRBFN helps in selecting proper samples to learn based on its current knowledge and evolve the architecture automatically. Since, the classifier developed using PBL-McRBFN is efficient, we propose recursive feature elimination approach (called PBL-McRBFN-RFE) to identify most relevant brain regions responsible for PD prediction. The study has been conducted using the Parkinson’s Progression Markers Initiative (PPMI) data set. First, we conducted the study on PD prediction using the PBL-McRBFN classifier on the PPMI data set. We have also compared the results of the PBL-McRBFN classifier with the support vector machine (SVM) classifier. The study results clearly show that the PBL-McRBFN classifier produces better generalization performance on PD prediction. Finally, we use RFE approach with PBL-McRBFN to identify the brain regions responsible for PD. The PBL-McRBFN-RFE selected features indicate that the loss of gray matter in the superior temporal gyrus region may be responsible for the onset of PD, and is consistent with the earlier findings from medical research studies

Alzheimer's disease detection using a projection based learning meta-cognitive RBF network

In this paper, we present a novel approach with Voxel-Based Morphometry (VBM) detected features using a proposed `Projection Based Learning for Meta-cognitive Radial Basis Function Network (PBL-McRBFN)' classifier for the detection of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans. McRBFN emulates human-like meta-cognitive learning principles. As each sample is presented to the network, 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, its 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. Moreover, as samples with similar information are deleted, over-training is avoided. The PBL algorithm helps to reduce the computational effort used in training. For simulation studies, we have used well-known open access series of imaging studies data set. The performance of the PBL-McRBFN classifier is evaluated on complete morphometric features set obtained from the VBM analysis and also on reduced features sets from Independent Component Analysis (ICA). The performance evaluation study clearly indicates the superior performance of PBL-McRBFN classifier over results reported in the literature