Ensemble of Hybrid CNN-ELM Model for Image Classification
Suresh Prasad Kannojia, Gaurav Jaiswal · 2018
To leverage feature representation of CNN and fast classification learning of ELM, Ensemble of Hybrid CNN-ELM model is proposed for image classification. In this model, image representation features are learned by Convolutional Neural Network (CNN) and fed to Extreme Learning Machine (ELM) for classification. Three hybrid CNN-ELMs are ensemble in parallel and final output is computed by majority voting ensemble of these classifier’s outputs. The experiments show this ensemble model improves the classifier’s classification confidence and accuracy. This model has been benchmarked on MNIST benchmark dataset and effectively improved the accuracy in comparison of single hybrid CNN-ELM classifier with accuracy upto 99.33%. Proposed ensemble model has been also compared with Core CNN, Core ELM, Hybrid CNN-ELM and achieves competitive accuracy.