JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Case Study on Machine Learning Applications and Performance Improvement in Learning Algorithm
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Digital Convergence
  • Volume 14, Issue 2,  2016, pp.245-258
  • Publisher : The Society of Digital Policy and Management
  • DOI : 10.14400/JDC.2016.14.2.245
 Title & Authors
A Case Study on Machine Learning Applications and Performance Improvement in Learning Algorithm
Lee, Hohyun; Chung, Seung-Hyun; Choi, Eun-Jung;
  PDF(new window)
 Abstract
This paper aims to present the way to bring about significant results through performance improvement of learning algorithm in the research applying to machine learning. Research papers showing the results from machine learning methods were collected as data for this case study. In addition, suitable machine learning methods for each field were selected and suggested in this paper. As a result, SVM for engineering, decision-making tree algorithm for medical science, and SVM for other fields showed their efficiency in terms of their frequent use cases and classification/prediction. By analyzing cases of machine learning application, general characterization of application plans is drawn. Machine learning application has three steps: (1) data collection; (2) data learning through algorithm; and (3) significance test on algorithm. Performance is improved in each step by combining algorithm. Ways of performance improvement are classified as multiple machine learning structure modeling, machine learning structure modeling, and so forth.
 Keywords
machine learning classification;machine learning modeling;performance improvement;optimization;
 Language
Korean
 Cited by
 References
1.
SAS Institute, "Machine Learning: What it is & why it matters", http://www.sas.com/en_us/insights/analytics/machine-learning.html (December 1, 2015)

2.
A. L. Samuel, "Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development", Vol. 3, No. 3, 1959.

3.
B.-T. Zhang, "Next-Generation Machine Learning Technologies. Journal of Computing Science and Engineering", Vol. 25, No. 3, pp. 96-107, 2007.

4.
Jingu Lee, "Comparative Study of Various Machine Learning Techniques for Infrasound Signals Associated with Volcanic Eruptions. Master's thesis". Korea University. 2014.

5.
A. Cannata, P. Montalto, M. Aliotta, C. Cassisi, A. Pulvirenti, E. Privitera and D. Patane, "Clustering and classification of infrasonic events at Mount Etna using pattern recognition technique. Geophysical Journal International", Vol. 185, No. 1, pp. 253-264, 2011. crossref(new window)

6.
Sangbeom Kim, "A Study on Constitutional Classification Using Speech Features and Machine Learning Methods. Master's thesis", Daejon University. 2012.

7.
Jeongmin Choi, "Vision based self learning mobile robot based on machine learning algorithm. Master's thesis", Chungnam National University. 2009.

8.
C. Gaskett, L. Fletcher, and A. Zelinsky, "Reinforcement Learning for a Vision Based Mobile Robot. Intelligent Robots and Systems, IEEE International Conference on", pp. 403-409, 2000.

9.
C. V. Regueiro, J. E. Domenech, R. Iglesias, and J. Correa, "Acquiring contour following behavior in robotics through Q-learning and image-based states. PWASET", Vol 15, 2006.

10.
Sangjun Park, "Content-Based Classification of Musical Genre using Machine Learning. Master's thesis", Seoul National University. 2002.

11.
Shin Hwi Yun, "Estimation of Vessel Service Time Base on Machine Learning. Master's thesis", Pusan National University. 2009.

12.
Mi-Sun Moon, Kang Song, and Dong-Ho Song, "Aviation Application: UAS Automatic Control Parameter Tuning System using Machine Learning Module. Journal of the Korean Institute of Navigation", Vol. 14, No. 6, pp. 874-881, 2010.

13.
Y. Abe, M. Konosho, J. Imai, R. Hasagawa, M. Watanabe and H. Kamiio, "PID Gain Tuning Method for Oil Refining Controller based on Neural Networks. Proc. of the Second Intl. Conf. on Innovative Computing, Information and Control", 2007.

14.
J. Lu, O. Ling and J. Zhang, "Lateral Control LawDesign for Helicopter Using Radial Basis Function Neural Network. Proc of the IEEE Intl. Conf. of Automation and Logistic", August. 2007.

15.
Gyeong-Woo Gang, "Development of bio-signal based eye-tracking system using dual machine learning structure. Master's thesis", Catholic University. 2013.

16.
Hyunsin Park, Sungwoong Kim, Minho Jin, and Chang D. Yoo, "The latest machine learning-based speech recognition technology trends. The Magazine of the IEEK", Vol. 41, No. 3, pp. 18-27, 2014.

17.
Haneol Kim, "Machine learning to detect garbage collecting SSDs and its use to increase performance predictability. Master's thesis", Hongik University. 2015.

18.
Dongjin Jung, "A Study on Effectiveness of Machine Learning Method for Fall Detection based on Extracted Feature from Acceleration Data. Master's thesis", Inje University. 2015.

19.
Byoung-Won Min, Yong-Sun Oh, "Improvement of Personalized Diagnosis Method for U-Health. Journal of Korea Contents Association", Vol. 10, No. 10, pp. 54-67, 2010.

20.
Sang Cheol Park, Myung Eun Lee, Soo Hyung Kim, In Seop Na, and Yanjuan Chen, "Machine Learning for Medical Image Analysis. Journal of Computing Science and Engineering", Vol. 39, No. 3, pp. 163-174, 2012.

21.
S. C. Park, J. Pu, and B. Zheng, "Improving Performance of Computer-Aided Detection Scheme by Combining Results from Two Machine Learning Classifiers. Academic Radiology", Vol. 16, No. 3, pp. 266-274, 2009. crossref(new window)

22.
Seunghak Yu, Sangheon Baek, and Seonro Yun, "Survey of Analysis Methods for Understanding Gene Expression Regulation Mechanisms Using Ensemble Learning. Journal of Computing Science and Engineering", Vol. 32, No. 10, pp. 38-43, 2014.

23.
Dongyoung Kim, Jeawon Park, and Jaehyun Choi, "A Comparative Study between Stock Price Prediction Models Using Sentiment Analysis and Machine Learning Based on SNS and News Articles. Journal of Information Technology Services," Vol. 13, No. 3, pp. 221-233, 2014.

24.
Joa-Sang Lim, Jin-Man Kim, "An Empirical Comparison of Machine Learning Models for Classifying Emotions in Korean Twitter. Journal of Korea Multimedia Society", Vol. 17, No. 2, pp. 232-239, 2014. crossref(new window)

25.
L. Zhang, R. Ghosh, M. Dekhil, M. Hsu, and B. Liu, "Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis. Technical Report HPL-2011, HP Laboratories", Vol. 89, 2011.

26.
Choen Lee Jeong, "Application of Artificial Neural Networks Technique for the Improvement of Flood Forecasting and Warning System. Ph. D. dissertation", Dongshin University. 2010.

27.
Coulibaly P, Anctil F, and Bobee B, "Daily Reservoir Inflow Forecasting using Artificial Neural Networks with stopped Training Approach. Journal of Hydrology", Vol. 230, No. 3, pp. 224-257, 2000.

28.
French, M. N., Krajewski, W. F., and Cuykendall R. R., "Rainfall forecasting in space and time using a neural network. Journal of Hydrology", Vol. 137, pp. 1-31, 1992. crossref(new window)

29.
Jae-Hyun Seo, Yong-Hyuk Kim, "A Survey on Rainfall Forecast Algorithms Based on Machine Learning Technique. Proceedings of KIIS Fall Conference", Vol. 21, No. 2, pp. 218-221, 2011.

30.
M. N. French, W. F. Krajewski, and R. R. Cuykendall, "Rainfall Forecasting in Space and Time Using a Neural Network. Journal of Hydrology", Vol. 137, No. 1-4, pp. 1-31, 1992. crossref(new window)

31.
Junyeob Yim, Byung-Yeon Hwang, "Predicting Movie Success based on Machine Learning Using Twitter. Journal of Korea Information Processing Society", Vol. 3, No. 7, pp. 263-270, 2014.

32.
Seong-Jin Kim, Cheol-Young Ock, "Analysis of Korean Language Parsing System and Speed Improvement of Machine Learning using Feature Module. Journal of The Institute of Electronics and Information Engineers", Vol. 51, No. 8, pp. 66-74, 2014.

33.
Miikkulainen, R. and Dyer, M. G, "Natural Language processing with modular neural networks and distributed lexicon. Cognitive Science", Vol. 15, No. 3, pp. 343-399, 1991. crossref(new window)

34.
ChungHee Lee, YoungHoon Seo, and HyunKi Kim, "Competition Relation Extraction based on Combining Machine Learning and Filtering. Journal of Computing Science and Engineering", Vol. 42, No. 3, pp. 367-378, 2015.

35.
Jaeuk Seol, "Clinical causal relationship extraction based on machine learning and rule from discharge summaries. Master's thesis", Chonbuk National University. 2014.