DOI QR코드

DOI QR Code

Predicting Employment Earning using Deep Convolutional Neural Networks

딥 컨볼루션 신경망을 이용한 고용 소득 예측

  • Received : 2018.05.07
  • Accepted : 2018.06.20
  • Published : 2018.06.28

Abstract

Income is a vital aspect of economic life. Knowing what their income will help people create budgets that allow them to pay for their living expenses. Income data is used by banks, stores, and service companies for marketing purposes and for retaining loyal customers; it is a crucial demographic element used at a wide variety of customer touch points. Therefore, it is essential to be able to make income predictions for existing and potential customers. This paper aims to predict employment earnings or income based on history, and uses machine learning techniques such as SVMs (Support Vector Machines), Gaussian, decision tree and DCNNs (Deep Convolutional Neural Networks) for predicting employment earnings. The results show that the DCNN method provides optimum results with 88% compared to other machine learning techniques used in this paper. Improvement of the data length such PCA has the potential to provide more optimum result.

Keywords

Income prediction;DCNN;SVM;Gaussian;Decision Tree

Acknowledgement

Supported by : National Research Foundation of Korea

References

  1. Cambridge University Press. (2008). Cambridge online dictionary, Cambridge Dictionary online. http://www.temoa.info/node/324
  2. Case, K. & Fair, R. (2007). Principles of Economics. Upper Saddle River, NJ: Pearson Education. 54.
  3. A. Lazar. (2004). Income prediction via support vector machine. 2004 International Conference on Machine Learning and Applications, Proceedings, 143-149.
  4. Conneau, A., Schwenk, H., Barrault, L. & Lecun, Y. (2017). Very Deep Convolutional Networks for Text Classification. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. 1. Long Papers. DOI:10.18653/v1/e17-1104 https://doi.org/10.18653/v1/e17-1104
  5. A. Kibekbaev & E. Duman. (2015). Benchmarking Regression Algorithms for Income Prediction Modeling. 2015 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, 180-185. DOI: 10.1109/CSCI.2015.162 https://doi.org/10.1109/CSCI.2015.162
  6. K. Chen, L. Tian, H. Ding. M. Cai, L. Sun, S. Liang & Q. Huo (2017). A Compact CNN-DBLSTM Based Character Model for Online Handwritten Chinese Text Recognition, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, 1068-1073.
  7. Bjelland, J., Reme B.A., Iqbal A. & Jahani, E. (2016), Deep learning applied to mobile phone data for Individual income classification. International conference on Artificial Intelligence: Technologies and Applications (ICAITA), Atlantic Press, 96-99.
  8. The Data Science Blog. (2018), An Intuitive Explanation of Convolutional Neural Networks. https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
  9. DL4J (2018). A Beginner's Guide to Deep Convolutional Neural Networks (CNNs) - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM. https://deeplearning4j.org/convolutionalnetwork
  10. Besbes, A. (2018). Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras. https://www.kdnuggets.com/2017/11/understanding-dee p-convolutional-neural-networks-tensorflow-keras.html
  11. Namatevs, I. (2017). Deep Convolutional Neural Networks: Structure, Feature Extraction and Training. Information Technology and Management Science, 20(1), 40-47 .
  12. B. E. Boser, I. M. Guyon, and V. N. Vapnik.(1992), A training algorithm for optimal margin classifiers, COLT '92 Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, PA, ACM Press, 144-152.
  13. K. Nurhanim, I. Elamvazuthi, L. I. Izhar and T. Ganesan. (2017). Classification of human activity based on smartphone inertial sensor using support vector machine, 2017 IEEE 3rd International Symposium in Robotics and Manufacturing Automation (ROMA), Kuala Lumpur, Malaysia, 1-5.
  14. Dataaspirant. (2017). How the Naive Bayes Classifier works in Machine Learning. http://dataaspirant.com/2017/02/06/naive-bayes-classifier-machine-learning/
  15. Analytics Vidhya Content Team. (2016). A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python). https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/
  16. Albelwi, S. and Mahmood, A. (2017). A Framework for Designing the Architectures of Deep Convolutional Neural Networks. Entropy, 19(6), 242. https://doi.org/10.3390/e19060242
  17. G. D. Kim, Y. H. Kim.(2017). A Survey on Oil Spill and Weather Forecast Using Machine Learning Based on Neural Networks and Statistical Methods, Journal of the Korea Convergence Society, 8(10), 1-8. https://doi.org/10.15207/JKCS.2017.8.10.001
  18. C. J. Lee,. G. D. Kim, Y. H. Kim, (2017). Performance Comparison of Machine Learning Based on Neural Networks and Statistical Methods for Prediction of Drifter Movement, .Journal of the Korea Convergence Society. 8(10), 45-52. https://doi.org/10.15207/JKCS.2017.8.10.045
  19. H. J. Yoon. (2017). Classification for early diagnosis for breast cancer base on Neural Network, Journal of the Korea Convergence Society, 8(12), 49-53. https://doi.org/10.15207/JKCS.2017.8.12.049
  20. K. T. Kim, J. Y. Choi. (2018). Facial Local Region Based Deep Convolutional Neural Networks for Automated Face Recognition, Journal of the Korea Convergence Society, 9(4), 47-55. https://doi.org/10.15207/JKCS.2018.9.4.047