Research Article
Year 2016,
Volume: 4 Issue: Special Issue-1, 23 - 26, 26.12.2016
Abstract
References
- P. Cortez, A. Cerderia, F. Almeida, T. Matos, and J. Reis, “Modelling wine preferences by data mining from physicochemical properties,” In Decision Support Systems, Elsevier, 47 (4): 547-553. ISSN: 0167-9236.
- S. Ebeler, “Linking Flavour Chemistry to Sensory Analysis of Wine,” in Flavor Chemistry, Thirty Years of Progress, Kluwer Academic Publishers, 1999, pp. 409-422.
- V. Preedy, and M. L. R. Mendez, “Wine Applications with Electronic Noses,” in Electronic Noses and Tongues in Food Science, Cambridge, MA, USA: Academic Press, 2016, pp. 137-151.
- A. Asuncion, and D. Newman (2007), UCI Machine Learning Repository, University of California, Irvine, [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html
- S. Kallithraka, IS. Arvanitoyannis, P. Kefalas, A. El-Zajouli, E. Soufleros, and E. Psarra, “Instrumental and sensory analysis of Greek wines; implementation of principal component analysis (PCA) for classification according to geographical origin,” Food Chemistry, 73(4): 501-514, 2001.
- N. H. Beltran, M. A. Duarte- MErmound, V. A. S. Vicencio, S. A. Salah, and M. A. Bustos, “Chilean wine classification using volatile organic compounds data obtained with a fast GC analyzer,” Instrum. Measurement, IEEE Trans., 57: 2421-2436, 2008.
- 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.
- 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.
- UCI Machine Learning Repository, Wine quality data set, [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Wine+Quality.
- J. Han, M. Kamber, and J. Pei, “Classification: Basic Concepts,” in Data Mining Concepts and Techniques, 3rd ed., Waltham, MA, USA: Morgan Kaufmann, 2012, pp. 327-393.
- J. Han, M. Kamber, and J. Pei, “Classification: Advanced Methods,” in Data Mining Concepts and Techniques, 3rd ed., Waltham, MA, USA: Morgan Kaufmann, 2012, pp. 393-443.
- W. L. Martinez, A. R. Martinez, “Supervised Learning” in Computational Statistics Handbook with MATLAB, 2nd ed., Boca Raton, FL, USA: Chapman & Hall/CRC, 2007, pp. 363-431.
Year 2016,
Volume: 4 Issue: Special Issue-1, 23 - 26, 26.12.2016
Abstract
The main purpose of this study is to predict wine quality based on
physicochemical data. In this study, two large separate data sets which were
taken from UC Irvine Machine Learning Repository were used. These data sets
contain 1599 instances for red wine and 4898 instances for white wine with 11
features of physicochemical data such as alcohol, chlorides, density, total
sulfur dioxide, free sulfur dioxide, residual sugar, and pH. First, the
instances were successfully classified as red wine and white wine with the
accuracy of 99.5229% by using Random Forests Algorithm. Then, the following
three different data mining algorithms were used to classify the quality of
both red wine and white wine: k-nearest-neighbourhood, random forests and
support vector machines. There are 6 quality classes of red wine and 7 quality
classes of white wine. The most successful classification was obtained by using
Random Forests Algorithm. In this study, it is also observed that the use of
principal component analysis in the feature selection increases the success
rate of classification in Random Forests Algorithm.
References
- P. Cortez, A. Cerderia, F. Almeida, T. Matos, and J. Reis, “Modelling wine preferences by data mining from physicochemical properties,” In Decision Support Systems, Elsevier, 47 (4): 547-553. ISSN: 0167-9236.
- S. Ebeler, “Linking Flavour Chemistry to Sensory Analysis of Wine,” in Flavor Chemistry, Thirty Years of Progress, Kluwer Academic Publishers, 1999, pp. 409-422.
- V. Preedy, and M. L. R. Mendez, “Wine Applications with Electronic Noses,” in Electronic Noses and Tongues in Food Science, Cambridge, MA, USA: Academic Press, 2016, pp. 137-151.
- A. Asuncion, and D. Newman (2007), UCI Machine Learning Repository, University of California, Irvine, [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html
- S. Kallithraka, IS. Arvanitoyannis, P. Kefalas, A. El-Zajouli, E. Soufleros, and E. Psarra, “Instrumental and sensory analysis of Greek wines; implementation of principal component analysis (PCA) for classification according to geographical origin,” Food Chemistry, 73(4): 501-514, 2001.
- N. H. Beltran, M. A. Duarte- MErmound, V. A. S. Vicencio, S. A. Salah, and M. A. Bustos, “Chilean wine classification using volatile organic compounds data obtained with a fast GC analyzer,” Instrum. Measurement, IEEE Trans., 57: 2421-2436, 2008.
- 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.
- 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.
- UCI Machine Learning Repository, Wine quality data set, [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Wine+Quality.
- J. Han, M. Kamber, and J. Pei, “Classification: Basic Concepts,” in Data Mining Concepts and Techniques, 3rd ed., Waltham, MA, USA: Morgan Kaufmann, 2012, pp. 327-393.
- J. Han, M. Kamber, and J. Pei, “Classification: Advanced Methods,” in Data Mining Concepts and Techniques, 3rd ed., Waltham, MA, USA: Morgan Kaufmann, 2012, pp. 393-443.
- W. L. Martinez, A. R. Martinez, “Supervised Learning” in Computational Statistics Handbook with MATLAB, 2nd ed., Boca Raton, FL, USA: Chapman & Hall/CRC, 2007, pp. 363-431.
There are 12 citations in total.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 26, 2016 |
| Published in Issue | Year 2016 Volume: 4 Issue: Special Issue-1 |