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


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.

소득은 경제생활에서 중요하다. 소득을 예측할 수 있으면, 사람들은 음식, 집세와 같은 생활비를 지불 할 수 있는 예산을 세울 수 있을 뿐 아니라, 다른 재화 또는 비상사태를 위한 돈을 별도로 저축 할 수 있다. 또한 소득수준은 은행, 상점 및 서비스 회사에서 마케팅 목적 및 충성도가 높은 고객을 유치하는 데 활용 된다. 이는 소득이 다양한 고객 접점에서 사용되는 중요한 인구 통계 요소이기 때문이다. 따라서 기존 고객 및 잠재 고객에 대한 수입 예측이 필요하다. 이 연구에서는 소득을 예측하기 위해 SVM (Support Vector Machines), Gaussian, 의사 결정 트리, DCNN (Deep Convolutional Neural Networks)과 같은 기계 학습 기법을 사용하였다. 분석 결과 DCNN 방법이 본 연구에서 사용 된 다른 기계 학습 기법에 비해 최적의 결과(88%)를 제공하는 것으로 나타났다. 향후 PCA 같이 데이터 크기를 향상 시킨다면 더 좋은 연구 결과를 제시할 수 있을 것이다.



Supported by : National Research Foundation of Korea


  1. Cambridge University Press. (2008). Cambridge online dictionary, Cambridge Dictionary online.
  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
  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
  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.
  9. DL4J (2018). A Beginner's Guide to Deep Convolutional Neural Networks (CNNs) - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM.
  10. Besbes, A. (2018). Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras. 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.
  15. Analytics Vidhya Content Team. (2016). A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python).
  16. Albelwi, S. and Mahmood, A. (2017). A Framework for Designing the Architectures of Deep Convolutional Neural Networks. Entropy, 19(6), 242.
  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.
  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.
  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.
  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.