A Sequential Projection Based Meta-cognitive Learning in RBF Network for Classification Problems

In this paper, we present a sequential projection based meta-cognitive learning algorithm in radial basis function network for classification problems, referred as PBL-McRBFN. The algorithm is inspired by human meta-cognitive learning principles and has two components namely a cognitive component and a meta-cognitive component. The cognitive component is a single hidden layer radial basis function network with evolving architecture. 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. In addition, sample overlapping conditions and past knowledge of the samples in the form of pseudo samples are used for proper initialization of new hidden neurons, to minimize the misclassification. The parameter update strategy uses projection based direct minimization of hinge loss error. The interaction of the cognitive component and the meta-cognitive component addresses the what-to-learn, whento-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository. The statistical performance evaluation on these problems has proven the superior performance of PBLMcRBFN classifier over results reported in the literature. Also, we evaluated the performance of the proposed algorithm on a practical Alzheimer’s disease detection problem. The performance results on open access series of imaging studies and Alzheimer’s disease neuroimaging initiative data sets which are obtained from different demographic regions clearly show that PBL-McRBFN can handle a problem with change in distribution.