LSTSVM classifier with enhanced features from pre-trained functional link network

Abstract

In this paper, we propose an improved model for the classification problems. We use least squares twin support vector machines (LSTSVM) and pre-trained functional link to enhance the feature space. LSTSVM algorithm is used in many real world classification problems as it has lower computational complexity and solves system of linear equations instead of solving quadratic programming problems (QPPs). Since neural network models provide implicit feature representation and is one of the reasons for the success of neural networks. Here, we propose a model wherein the input feature space is enhanced by the pre-trained functional link network. Weights are generated by LSTSVM, and a non-linear function is applied on the product between input features and the weights to get the enhanced features. These features are concatenated with the input features to get the extended feature space. Final classification is done by LSTSVM based on these extended features. Numerical experiments and statistical tests conducted show that the proposed model outperforms the baseline methods. © 2020 Elsevier B.V.