  Ensemble techniques are frequently encountered in machine learning and
engineering problems since the method combines different models and produces an
optimal predictive solution. The ensemble concept can be adapted to deep
learning models to provide robustness and reliability. Due to the growth of the
models in deep learning, using ensemble pruning is highly important to deal
with computational complexity. Hence, this study proposes a mathematical model
which prunes the ensemble of Convolutional Neural Networks (CNN) consisting of
different depths and layers that maximizes accuracy and diversity
simultaneously with a sparse second order conic optimization model. The
proposed model is tested on CIFAR-10, CIFAR-100 and MNIST data sets which gives
promising results while reducing the complexity of models, significantly.
