A New Channel Boosted Convolutional Neural Network using Transfer Learning

We present a novel architectural enhancement of Channel Boosting in a deep convolutional neural network (CNN). This idea of Channel Boosting exploits both the channel dimension of CNN (learning from multiple input channels) and Transfer learning (TL). TL is utilized at two different stages; channel generation and channel exploitation. In the proposed methodology, a deep CNN is boosted by various channels available through TL from already trained Deep Neural Networks, in addition to its original channel. The deep architecture of CNN then exploits the original and boosted channels down the stream for learning discriminative patterns. Churn prediction in telecom is a challenging task due to the high dimensionality and imbalanced nature of the data. Therefore, churn prediction data is used to evaluate the performance of the proposed Channel Boosted CNN (CB CNN). In the first phase, informative discriminative features are being extracted using a stacked autoencoder, and then in the second phase, these features are combined with the original features to form Channel Boosted images. Finally, the knowledge gained by a pretrained CNN is exploited by employing TL. The results are promising and show the ability of the Channel Boosting concept in learning complex classification problems by discerning even minute differences in churners and nonchurners. The proposed work validates the concept observed from the evolution of recent CNN architectures that the innovative restructuring of a CNN architecture may increase the networks representative capacity.