RFSEN-ELM: SELECTIVE ENSEMBLE OF EXTREME LEARNING MACHINES USING ROTATION FOREST FOR IMAGE CLASSIFICATION
Zhiyu Zhou, Chen Ji, Yacheng Song, Zefei Zhu, Xiangqi Liu · Neural Network World · 2017
Extreme learning machine (ELM), as a new learning mechanism for single hidden layer feedforward neural networks (SLFNs), has shown its good performance, such as fast computation speed and good generalization performance. However, the weak robustness of ELM is an unavoidable defect for image classification tasks. Thus, we propose a novel ensemble method combined rotation forest and selective ensemble model to overcome this problem in this paper. Firstly, ELM and rotation forest are integrated to construct an ensemble classifier (RF-ELM), which combines the advantages of both rotation forest and ELM. The purpose of rotation forest here is to enhance the diversity of each base classifier, thus improving the robustness of classification. Then several ELMs are removed from the ensemble pool by using genetic algorithm (GA) based selective ensemble model to further enhance the generalization performance. Finally, the remaining ELMs are grouped as a selected ensemble classifier (RFSEN-ELM) for image classification. The performance is analysed and compared with several existing methods on benchmark datasets and the experimental results demonstrate that the proposed algorithm substantially improves the accuracy and robustness of classification at an acceptable level of training cost.