Soft Meta-Cognitive Neural Network for Classification Problems

Abstract

Classification problems in a sequential framework is considered as an important field in pattern recognition. One of the main concerns in these types of problems is overtraining. In metacognitive neural networks (McNN), overtraining could be avoided by using the confidence of classifier (CoC) measure, which assigns a value between [0], [1] to class label. In this paper, a more accurate measure of CoC for McNN is presented. In addition, the hinge loss function which has no particular probabilistic interpretation is replaced by cross-entropy loss, which the output layer of the Soft meta-cognitive neural network (SMcNN) is a SoftMax layer. The classification performance is improved by applying the proposed SMcNN to well-known vCI datasets and comparing the results to McNN, SVM, and some other classifiers.