DOI QR코드

DOI QR Code

Performance Comparison Analysis of AI Supervised Learning Methods of Tensorflow and Scikit-Learn in the Writing Digit Data

필기숫자 데이터에 대한 텐서플로우와 사이킷런의 인공지능 지도학습 방식의 성능비교 분석

  • Jo, Jun-Mo (Dept. Electronic Engineering, TongMyong University)
  • Received : 2019.05.09
  • Accepted : 2019.08.15
  • Published : 2019.08.31

Abstract

The advent of the AI(: Artificial Intelligence) has applied to many industrial and general applications have havingact on our lives these days. Various types of machine learning methods are supported in this field. The supervised learning method of the machine learning has features and targets as an input in the learning process. There are many supervised learning methods as well and their performance varies depends on the characteristics and states of the big data type as an input data. Therefore, in this paper, in order to compare the performance of the various supervised learning method with a specific big data set, the supervised learning methods supported in the Tensorflow and the Sckit-Learn are simulated and analyzed in the Jupyter Notebook environment with python.

최근에는 인공지능의 도래로 인하여 수많은 산업과 일반적인 응용에 적용됨으로써 우리의 생활에 큰 영향을 발휘하고 있다. 이러한 분야에 다양한 기계학습의 방식들이 제공되고 있다. 기계학습의 한 종류인 지도학습은 학습의 과정 중에 특징값과 목표값을 입력으로 가진다. 지도학습에도 다양한 종류가 있으며 이들의 성능은 입력데이터인 빅데이터의 특성과 상태에 좌우된다. 따라서, 본 논문에서는 특정한 빅 데이터 세트에 대한 다수의 지도학습 방식들의 성능을 비교하기 위해 텐서플로우(Tensorflow)와 사이킷런(Scikit-Learn)에서 제공하는 대표적인 지도학습의 방식들을 이용하여 파이썬언어와 주피터 노트북 환경에서 시뮬레이션하고 분석하였다.

Keywords

KCTSAD_2019_v14n4_701_f0001.png 이미지

Fig. 1 Architecture of KSOM controller[3]

KCTSAD_2019_v14n4_701_f0002.png 이미지

Fig. 2 Example of the handwritten digit data

KCTSAD_2019_v14n4_701_f0003.png 이미지

Fig. 3 Comparing training methods with different quantity of input data

KCTSAD_2019_v14n4_701_f0004.png 이미지

Fig. 4 Comparing kneighbor with other methods

Table 1. Contents of the dataset

KCTSAD_2019_v14n4_701_t0001.png 이미지

Table 1. Euclidean distance method

KCTSAD_2019_v14n4_701_t0002.png 이미지

Table 2. Result of the training methods

KCTSAD_2019_v14n4_701_t0003.png 이미지

References

  1. Y. Jung and Y. Bae, "Analysis of Fault Diagnosis for Current and Vibration Signals in Pumps and Motors using a Reconstructed Phase Portrait," Int. J. of Fuzzy Logic and Intelligent Systems, vol. 15, no. 3, 2015, pp. 166-171. https://doi.org/10.5391/IJFIS.2015.15.3.166
  2. R. Sathya, and A. Annamma, "Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification," International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 2, 2013, pp. 34-38.
  3. R. Sathya and A. Abraham, "Unsupervised Control Paradigm for Performance Evaluation," International Journal of Computer Application, vol. 44, no. 20, 2012, pp. 27-31. https://doi.org/10.5120/6380-8850
  4. C. Neocleous, and C. Schizas, "Artificial Neural Network Learning: A Comparative, Methods and Applications of Artificial Intelligence.," Hellenic Conference on Artificial Intelligence SETN, Springer, 2002.
  5. N. Kim and Y. Bae, "Status Diagnosis of Pump and Motor Applying K-Nearest Neighbors," J. of the Korea Institute of Electronic Communication Science, vol. 13, no. 6, 2018, pp. 1249-1255. https://doi.org/10.13067/JKIECS.2018.13.6.1249
  6. J. M. Keller, M. R. Gray, and J. A. Givens, "A Fuzzy K-Nearest Neighbor Algorithm," IEEE Trans. Systems, Man, and Cybernetics, vol. 15, no. 4, 1985, pp. 581-585.
  7. S. Bang, "Implementation of Image based Fire Detection System Using Convolution Neural Network," J. of the Korea Institute of Electronic Communication Science, vol. 12, no. 2, 2017, pp. 331-336. https://doi.org/10.13067/JKIECS.2017.12.2.331
  8. Y. Kim, S. Park, and D. Kim, "Research on Robust Face Recognition against Lighting Variation using CNN," J. of the Korea Institute of Electronic Communication Science, vol. 12, no. 2, 2017, pp. 325-330. https://doi.org/10.13067/JKIECS.2017.12.2.325
  9. C. Jung, R. Jang, D. Nyang, and K. Lee " A Study of User Behavior Recognition-Based PIN Entry Using Machine Learning Technique," Korea Information Processing Society review, computer and communication systems, vol. 7, no. 5, 2018, pp. 127-136.
  10. G. Lee, H. Ha, H. Hong, and H. Kim "Exploratory Research on Automating the Analysis of Scientific Argumentation Using Machine Learning," J. of the Korean Association for Science Education, vol. 38, no. 2, 2018, pp. 219-234. https://doi.org/10.14697/JKASE.2018.38.2.219
  11. S. Shamim, M. Miah, A. Sarker, and M. Rana, "Handwritten Digit Recognition Machine Algorithms," Global Journal of Computer Science and Technology, vol. 18, 2018, pp. 17-23. https://doi.org/10.24215/16666038.18.e17