Ensemble of classification models with weighted functional link network

Abstract

Ensemble classifiers with random vector functional link network have shown improved performance in classification problems. In this paper, we propose two approaches to solve the classification problems. In the first approach, the original input space’s data points are mapped explicitly into a randomized feature space via neural network wherein the weights of the hidden layer are generated randomly. After feature projection, classification models twin bounded support vector machines (SVM), least squares twin SVM, twin k-class SVM, least squares twin k-class SVM and robust energy based least squares twin SVM are trained on the extended features (original features and randomized features). In the second approach, twin bounded support vector machines (SVM), least squares twin SVM, twin k-class SVM, least squares twin k-class SVM and robust energy based least squares twin SVM models are used to generate the weights of the hidden layer architecture and the weights of output layer are optimized via closed form solution. The performance of both the proposed architectures is evaluated on 33 datasets — including datasets from the UCI repository and fisheries data (not in UCI). Both the experimental results and statistical tests conducted demonstrate that the proposed approaches perform significantly better than the other baseline models. We also analyze the effect of the number of enhanced features on the performance of the given models.