  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.
