RFSEN-ELM: SELECTIVE ENSEMBLE OF EXTREME LEARNING MACHINES USING ROTATION FOREST FOR IMAGE CLASSIFICATION
| Autor: | Zefei Zhu, Xiangqi Liu, Zhiyu Zhou, Yacheng Song, Ji Chen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science Computer Science::Neural and Evolutionary Computation 02 engineering and technology Machine learning computer.software_genre 020901 industrial engineering & automation Physics::Plasma Physics Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Extreme learning machine Ensemble forecasting Contextual image classification Artificial neural network business.industry General Neuroscience Pattern recognition Ensemble learning Hardware and Architecture Feedforward neural network 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) computer Software |
| Zdroj: | Neural Network World. 27:499-517 |
| ISSN: | 2336-4335 1210-0552 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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