Multiclass Classification with an Ensemble of Binary Classification Deep Networks

Deep neural network classifiers have been used frequently and are efficient. In multiclass deep network classifiers, the burden of classifying samples of different classes is put on a single classifier. As shown in this paper, the classification capability of deep networks can be further increased by using an ensemble of binary classification deep networks. In the proposed approach, a single (one-versus-all) deep network binary classifier is dedicated to each category classification. Subsequently, binary classification deep network ensembles have been investigated. Every network in an ensemble has been trained by a one-versus-all binary training technique using the Stochastic Gradient Descent with Momentum Algorithm. For classification of the test sample, the sample is presented to each network in the ensemble. After softmax-layer score voting, the network with the largest score is assumed to have classified the sample. Digit image recognition has been used for experimentation. Three datasets have been used for experimentation viz. the MATLAB Digit Image Dataset, the USPS+ Digit Image Dataset, and the MNIST Digit Image Dataset. The experiments demonstrate that given sufficient training, a Binary Classification Convolutional Neural Network (BCCNN) ensemble can outperform a conventional Multi-class Convolutional Neural Network (MCNN). In one of the experiments, it was noted that after training and testing of a BCCNN ensemble and an MCNN respectively on a subset of the MNIST Digit Image Dataset, the BCCNN ensemble gave a higher accuracy of 98.03% as compared to the MCNN which gave an accuracy of 97.90%. The architecture of the BCCNNs in an ensemble has also been modified in order to increase their recognition accuracy. On a large subset of the MNIST Digit Image Dataset, the modified BCCNN ensemble gave a higher accuracy of 98.50%, while as the MCNN gave an accuracy of 98.4875%.