Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Classification Evaluation Prediction Model by Cluster for Accuracy Measurement of Unsupervised Learning Data

비지도학습 데이터의 정확성 측정을 위한 클러스터별 분류 평가 예측 모델에 대한 연구

  • Jung, Se Hoon (Dept. of Multimedia Eng., Sunchon National University) ;
  • Kim, Jong Chan (Dept. of Computer Eng., Sunchon National University) ;
  • Kim, Cheeyong (Major of Game Animation Engineering, Dong-Eui University) ;
  • You, Kang Soo (Dept. of Library & Information Science, Jeon-ju University) ;
  • Sim, Chun Bo (Dept. of Multimedia Eng., Sunchon National University)
  • Received : 2018.05.21
  • Accepted : 2018.06.11
  • Published : 2018.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.

Keywords

References

  1. D.H. Shin, K.H. Choi, and C.B. Kim, "Deep Learning Model for Prediction Rate Improvement of Stock Price Using RNN and LSTM," Journal of Korea Information Technology Association, Vol. 15, No. 10, pp. 9-16, 2017.
  2. D.H. Shim, "Inductive Learning Using Theory- Refinement Knowledge-Based Artificial Neural Network," Journal of Korea Multimedia Society, Vol. 4, No. 3, pp. 280-285, 2001.
  3. S.B. Park and D.S. Yoo, "Multimedia Database Management Issues," Journal of Korea Multimedia Society, Vol. 21, No. 3, pp. 391-399, 2018.
  4. S.H. Jung, J.C. Kim, and C.B. Sim, “A Novel Data Prediction Model Using Data Weights and Neural Network Based on R for Meaning Analysis between Data,” Journal of Korea Multimedia Society, Vol. 18, No. 4, pp. 524- 532, 2015. https://doi.org/10.9717/kmms.2015.18.4.524
  5. J.W. Lee, An Automated Text Classification Method Using Multi Feature Extraction Module Based on Neural Network, Master's Thesis of Hanbat National University, 2016.
  6. S.M. Jung, Study on the Integrated System of Face Recognition and Speaker Verification Using Neural Network, Master's Thesis of Hongik University, 2015.
  7. H.W. Jung, Bankruptcy Prediction Based on Deep Learning Algorithm, Master's Thesis of Hanyang University, 2016.
  8. S.H. Yoo, DNN Based Customer Electric Load Forecasting, Master's Thesis of Sogang University, 2016.
  9. C. Lee Giles, Steve Lawrence, and A.C. Tsoi, "Noisy Time Series Prediction Using Recurrent Neural Networks and Grammatical Inference," Journal of Machine Learning, Vol. 44, No. 1, pp. 161-183, 2001. https://doi.org/10.1023/A:1010884214864
  10. S.H. Jung, K.J. Kim, J.C. Kim, C.Y. Kim, and C.B. Sim, "A Study on Recurrent Neural Network for Prediction and Accuracy Improvement of Data Classification Evaluation," Proceeding of the Fall Conference of the Korea Multimedia Society, pp. 49-51, 2017.
  11. Machine Learning Repository(2018). http://archive.ics.uci.edu/ml (accessed Feb., 15, 2018).
  12. S.H. Jung, A Novel on Hybrid Machine Learning Method Based on Big Data Mining, Doctor's Thesis of Sunchon National University, 2017.
  13. S.H. Jung, K.J. Kim, E.C. Lim, and C.B. Sim, "A Novel on Automatic K Value for Efficiency Improvement of K-means Clustering," Proceeding of International Conference on International Conference on Future Information Technology, International Conference on Multimedia and Ubiquitous Engineering, Vol. 448, pp. 181-186, 2017.