We present a novel architectural enhancement of Channel Boosting in deep convolutional neural network (CNN). This idea of Channel Boosting exploits both the channel dimension of CNN (learning from multiple channels) and Transfer learning (TL). TL is utilized at two different stages, channel generation and channel exploitation. A deep CNN is boosted by various channels available through TL from already trained Deep NN, in addition to its own 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 high dimensionality and imbalanced nature of the data and it is therefore used to evaluate the performance of the proposed Channel Boosted CNN (CB-CNN). In the first phase, discriminative informative features are being extracted using a staked autoencoder, and then in the second phase, these features are combined with the original features to form Channel Boosted images. Finally, a pre-trained CNN is exploited by employing TL to perform classification. The results are promising and show the ability of the Channel Boosting concept in learning complex classification problem by discerning even minute differences in churners and non-churners. The proposed work validates the concept observed from the evolution of recent CNN architectures that the innovative restructuring may increase the representative capacity of the network.
View on arXiv