An ensemble of deep learning architectures for automatic feature extraction

This paper presents a novel ensemble of deep learning architectures for automatic feature extraction. Many ensemble techniques have been recently proposed and successfully applied to real world applications. The existing ensemble techniques can achieve high accuracy however the accuracy depends on features they use and features are extracted by a separate model for feature extraction. As deep learning architectures such as Convolutional Neural Networks (CNNs) can automatically extract features, it is a good idea to explore their feature extraction ability in an ensemble. Therefore the purpose of this research is to propose an ensemble of CNNs and find out the answer of whether or not an ensemble of CNNs can perform better than the traditional ensemble techniques which use a separate feature extraction. To find an answer of the research question, an ensemble of CNNs, an ensemble of MLPs, a CNN and an MLP are implemented and evaluated on the same benchmark datasets. A large number of experiments were conducted and the results showed that the proposed ensemble of CNNs can automatically extract features and achieve better accuracy but takes a higher number of epochs than other ensembles on some real-world image datasets.