한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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회귀분석과 딥러닝의 예측 정확성에 대한 비교 그리고 딥러닝 모델 최적화를 위한 기법들의 중요성에 대한 실증적 분석

Comparison of Prediction Accuracy Between Regression Analysis and Deep Learning, and Empirical Analysis of The Importance of Techniques for Optimizing Deep Learning Models

  • 조민호 (중원대학교 컴퓨터공학과)
  • Min-Ho Cho (Dept. Computer System Engineering, JungWon University)
  • 투고 : 2023.02.02
  • 심사 : 2023.04.17
  • 발행 : 2023.04.30
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초록

인공지능 기법 중에서 딥러닝은 많은 곳에서 사용되어 효과가 입증된 모델이다. 하지만, 딥러닝 모델이 모든 곳에서 효과적으로 사용되는 것은 아니다. 이번 논문에서는 회귀분석과 딥러닝 모델의 비교를 통하여 딥러닝 모델이 가지는 한계점을 보여주고, 딥러닝 모델의 효과적인 사용을 위한 가이드를 제시하고자 한다. 추가로 딥러닝 모델의 최적화를 위해 사용되는 다양한 기법 중, 많이 사용되는 데이터 정규화와 데이터 셔플링 기법을 실제 데이터를 기반으로 비교 평가하여 딥러닝 모델의 정확성과 가치를 높이기 위한 기준을 제시하고자 한다.

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.

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참고문헌

  1. C. Kim, S, Choi and K. Kwahk, "Investigation of Research Trends in Information Systems Domain Using Topic Modeling and Time Series Regression Analysis," Journal of Digital Contests Society, vol. 18, no. 6, Oct. 2017, pp. 1143-1150. 
  2. J. Liu, X. Kong, F. Xia, X. Bai, L. Wang, Q. Qing and I. lee, "Artificial Intelligence in the 21st century," IEEE Access, vol. 6, Mar. 2018, pp. 34403-34421.  https://doi.org/10.1109/ACCESS.2018.2819688
  3. Y. Guo, Y. Liu, A. oerlemans, S. Lao, S. Wu and M. S. Lew, "Deep Learning for visual understanding : A review," Neurocomputing, vol. 187, Apr. 2016, pp. 27-48.  https://doi.org/10.1016/j.neucom.2015.09.116
  4. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senoir, V. Vanhoucke, P. Nguyen, T. N. Sainath and B. Kingbury, "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Processing Magazine, vol. 29, issue. 6, Nov. 2012, pp. 82-97.  https://doi.org/10.1109/MSP.2012.2205597
  5. S. Dreiseitl and L. O. Machado, "Logistic regression and artificial neural network classification models : a methodology review," Journal of Bio medical Informatics, vol. 32, issue. 5-6, Oct. 2002, pp. 352-359.  https://doi.org/10.1016/S1532-0464(03)00034-0
  6. M. Cho, "A study on the history, classification and development direction of artificial intelligence," J. of the Korea Institute of Electronic Communication Sciences, vol. 16, no. 2, April. 2021, pp. 307-312. 
  7. J. Moon and Y. Lee, "Artificial Intelligence Computing Platform Design for Underwater Localization" J. of the Korea Institute of Electronic Communication Sciences, vol. 17, no. 1, Feb, 2022, pp. 119-124. 
  8. C. Lee, H. Park, "A Comparative Study on the Accuracy of Important Statistical Prediction Technique of Marketing Data," J. of the Korea Institute of Electronic Communication Sciences, vol. 16, no. 3, June. 2021, pp. 519-524. 
  9. S. Jung and Y. Chung, "Comparison of audio event detection performance using DNN," J. of the Korea Institute of Electronic Communication Sciences, vol. 13, no. 3, June 2018, pp. 571-578. 
  10. M. Dahiya, "A Tool of Conversation : Chatbot," International Journal of Computer Sciences and Engineering, vol. 5, issue. 5, May 2017, pp. 158-161.  https://doi.org/10.26438/ijcse/v5i9.2126
  11. J. Jhang and C. Zong, "Deep Neural Networks in Machine Translation: An Overview," IEEE Intelligent Systems, vol. 30, issue. 5, Sept.-Oct. 2015, pp. 16-25. https://doi.org/10.1109/MIS.2015.69