Open Access Open Access  Restricted Access Subscription or Fee Access

A Machine Learning Based Approach to Multiclass Classification of Customer Loyalty Using Deep Nets

(*) Corresponding author

Authors' affiliations



Identification of customer’s loyalty is one of the most captivating area of today’s growing business scenario. For any organization, retaining customer is more important than exploring new customers. In this paper, Deep Belief Network (DBN) based approach is implemented for classifying customer loyalties. Training a Deep Belief Network (DBN) is a tedious task but once it trains, the accuracy of classification improves immensely. It also learns from its environment and does not need to be reprogrammed for new situations completely. After training, classifier relies on weight matrices to classify examples. The proposed approach is tested over real as well as sample datasets. The results so acquired are compared with Deep Neural Networks and Support Vector Machine based approaches, which shows Deep Belief Network (DBN) gives accuracy up to 99%.
Copyright © 2017 Praise Worthy Prize - All rights reserved.


Classification; Customer Loyalty; Deep Belief Network; Deep learning; Deep Neural Network; Machine Learning; Probabilistic Graphical Model; Restricted Boltzmann Machine

Full Text:



Arya, A., Agarwal, P., et al, Automatic Fuzzy Classification Tool for Customer Loyalty using Gaussian Membership Function,, (2010) Data Mining and Knowledge Engineering, 2(7), 168-173.

Arya A., Agarwal P., Fuzzy Decision Tree based Automatic Classifier for Customer Loyalty,(2010) In Proc. Of Intl. Conf. on Data Management.

Bojarski, Mariusz, et al. "Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car.", (2017)arXiv preprint arXiv:1704.07911.

AgarwalP., SuryaPrasadJ., Arya A., ANave Hopfield Neural Network Based Approach for Multiclass classification of Customer Loyalty, (2015) Foundation of Computer Science (FCS), NY, USA. Communications on Applied Electronics (CAE) 2(5),36-43.

Extending the Darch library for Deep Architecture 2015.pdf

Vieira, Armando, Predicting online user behavior using deep learning algorithms(2015)arXiv preprint arXiv:1511.06247

Hinton, G. E., Osindero, S., and Teh, Y. W., A fast learning algorithm for deep belief nets, (2006)Neural computation, 18(7), 1527-1554.

Han, J., Pei, J., and Kamber, M., Data mining: concepts and techniques, (2011) Morgan Kauffmann Publishers, Elsevier.

Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., and Shelhamer, E., cudnn: Efficient primitives for deep learning, (2014) arXiv preprint arXiv:1410.0759.

Peng, Y., and Flach, P. A., Soft discretization to enhance the continuous decision tree induction, (2011) Integrating Aspects of Data Mining, Decision Support and Meta-Learning, 1(109118), 34.

Ascarza, E., Neslin, S., Netzer, O., Anderson, Z., Fader, P., Gupta, S., ... & Provost, F. (2017) In Pursuit of Enhanced Customer Retention Management.

Lpez, J. J., Aguado, J. A., et al., HopfieldK-Means clustering algorithm: A proposal for the segmentation of electricity customers, (2011) Electric Power Systems Research, 81(2), 716724.

Glorot, X., Bordes, A., and Bengio, Y., Domain adaptation for largescale sentiment classification: A deep learning approach, (2011)In Proceedings of the 28th International Conference on Machine Learning (ICML-11) 513-520.

Tang, D., Wei, F., Qin, B., Liu, T., and Zhou, M., Coooolll: A deep learning system for Twitter sentiment classification, (2014) In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (pp. 208-212).

Zhou, S., Chen, Q., and Wang, X., Active deep learning method for semi-supervised sentiment classification, (2013) Neurocomputing, 120, 536546.

Fischer, A., and Igel, C., Training restricted Boltzmann machines: an introduction, (2014) Pattern Recognition, 47(1), 25-39.

Fischer, A., and Igel, C., An introduction to restricted Boltzmann machines, Progress in Pattern Recognition, Image Analysis, (2012) Computer Vision, and Applications (pp. 14-36). Springer Berlin Heidelberg.

Sa, Inkyu, et al, Deepfruits: A fruit detection system using deep neural networks, (2016)Sensors 16.8: 1222.

Shaham, U., Cloninger, A., and Coifman, R. R., Provable approximation properties for deep neural networks, (2015) arXiv preprint arXiv:1509.07385.

Le, Q. V., A Tutorial on Deep Learning Part 1: Nonlinear Classifiers and The Backpropagation Algorithm, (2015).

Schmidhuber, J., Deep learning in neural networks: An overview, Neural Networks, 61, 85117(2015).

Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., and Ma, Y., ”PCANet: A simple deep learning baseline for image classification?”, (2015)IEEE Transactions on Image Processing, 24(12), 50175032.

Ghasemi, Fahimeh, et al., The role of different sampling methods in improving biological activity prediction using deep belief network, (2017) Journal of Computational Chemistry 38.4: 195-203.

Bengio, Y., Learning deep architectures for AI, (2009)Foundations and trends in Machine Learning, 2(1), 1-127.

Deng, L., Three classes of deep learning architectures and their applications: a tutorial survey, (2012)APSIPA transactions on signal and information processing.

Hinton, G. E., and Salakhutdinov, R. R., A better way to pretrain deep boltzmann machines, (2012)In Advances in Neural Information Processing Systems, 2447-2455.

Havaei, Mohammad, et al. , Brain tumor segmentation with deep neural networks, (2017)Medical image analysis 35: 18-31.

Srivastava, N., and Salakhutdinov, R. R., Multimodal learning with Deep Boltzmann machines, (2012) In Advances in neural information processing systems, 2222-2230.

Jiang, Yu-Gang, et al, Exploiting feature and class relationships in video categorization with regularized deep neural networks, (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence.

Silver, David, et al, Mastering the game of Go with deep neural networks and tree search, (2016)Nature 529.7587: 484-489.

Wu, Chunyang, et al, Stimulated deep neural network for speech recognition, (2016)Proc. Interspeech.

Rojathai, S., Venkatesulu, M., An Effective Tamil Speech Word Recognition Technique with Aid of MFCC and HMM (Hidden Markov Model), (2013) International Review on Computers and Software (IRECOS), 8 (2), pp. 577-586.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize