Browse > Article
http://dx.doi.org/10.9708/jksci.2017.22.02.081

Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection  

Lee, Seunghan (Dept. of Computer Science and Engineering, Hanyang University)
Kang, Kyungtae (Dept. of Computer Science and Engineering, Hanyang University)
Noh, Dong Kun (Department of Smart Systems SW, Soongsil University)
Abstract
Nowadays wine is increasingly enjoyed by a wider range of consumers, and wine certification and quality assessment are key elements in supporting the wine industry to develop new technologies for both wine making and selling processes. There have been many attempts to construct a more methodical approach to the assessment of wines, but most of them rely on objective decision rather than subjective judgement. In this paper, we propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. We used sequential forward selection and decision tree for this purpose. Experiments with the wine quality dataset from the UC Irvine Machine Learning Repository demonstrate the accuracies of 76.7% and 78.7% for red and white wines respectively.
Keywords
Decision Tree; Wine Quality; Classification; Sequential Forward Selection;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 M. A. Hussain, M. K. Rao, and A. M. Mahmood, "An optimized approach to generate simplified decision trees," IEEE International Conference on Computational Intelligence and Computing Research, pp. 1-5, Dec. 2013
2 A. Asuncion and D. Newman, UC Irvine Machine Learning Repository, [Online] Available: http://archive.ics.uci.edu/ml/index.html.
3 S. Shanmuganathan, P. Sallis, and A. Narayanan, "Data mining techniques for modelling seasonal climate effects on grapevine yield and wine quality," IEEE International Conference on Computational Intelligence, Communication Systems and Networks, pp. 82-89, July 2010.
4 B. Chen, C. Rhodes, A. Crawford, and L. Hambuchen, "Wineinformatics: applying data mining on wine sensory reviews processed by the computational wine wheel," IEEE International Conference on Data Mining Workshop, pp. 142-149, Dec. 2014.
5 J. R. Quinlan, "Improved use of continuous attributes in C4.5," Journal of Artificial Intelligence Research 4, vol. 4, no. 1, pp. 77-90, Jan. 1996.   DOI
6 I. M. Mitchell, Machine Learning, McGraw-Hill International Editions, 1997
7 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann; Second Edition, 2005.
8 R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," In Proc. International Joint Conference on Artificial Intelligence, pp. 1137-1143, Aug. 1995.
9 M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update," ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10-18, June 1997.   DOI
10 WineKorea, [Online] Available: http://www.winekorea.asia/
11 A Comissao de Viticultura da Regiao dos Vinhos Verdes, [Online] Available: http://www.vinhoverde.pt/
12 C.M Lee and S.S Narayanan, "Toward detecting emotions in spoken dialogs," IEEE Transactions on Speech and Audio Processing, vol. 13, no. 2, pp. 293-303, May 2005   DOI
13 Chu Weibo, Zhu Bin B., Xue Feng, Guan Xiaohong, and Cai Zhongmin, "Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs," IEEE International Conference on Communications, pp. 1990-1994 June 2013.
14 Ladha, L., Deepa, T., "FEATURE SELECTION METHODS AND ALGORITHMS," International Journal on Computer Science & Engineering, Vol. 3, no. 5, pp. 1787-1797 , May 2011
15 Dimitrios Ververidis and Constantine Kotropoulos, "Sequential forward feature selection with low computational cost" European Signal Processing Conference, pp. 1-4, Sept. 2005
16 P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, "Using Data Mining for Wine Quality Assessment,"International Conference on Discovery Science pp. 66-79, Oct. 2009
17 P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, "Modeling wine preferences by data mining from physicochemical properties," Decision Support Systems, vol. 47, no. 4, pp. 547-553, Nov. 2009   DOI
18 Daniele Grifoni, Marco Mancini, Giampiero Maracchi, Simone Orlandini and Gaetano Zipoli, "Analysis of Italian Wine Quality Using Freely Available Meteorological Information," American Journal of Enology and Viticulture, pp. 339-346, Sep. 2006
19 Sungmain Myoung and Chang-Hwan Oh, "Pattern Recognition for Typification of Whiskies and Brandies in the Volatile Components using Gas Chromatographic Data" Journal of the Korea Society of Computer and Information, Vol. 21, no. 5 pp. 167-175, May 2016   DOI
20 Seok-Woo Jang, Moon-Haeng Hun and Gye-Younng Kim, "Effective Handwriting Verification through DTW and PCA" Journal of the Korea Society of Computer and Information, Vol. 14, no. 7 pp. 25-32, July. 2009
21 A. Abdelhalim and I. Traore, "A new method for learning decision trees from rules," IEEE International Conference on Machine Learning and Applications, pp. 693-698, Dec. 2009.