Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 23 - 26, 26.12.2016
https://doi.org/10.18201/ijisae.265954

Öz

Kaynakça

  • 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.

The Classification of White Wine and Red Wine According to Their Physicochemical Qualities

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 23 - 26, 26.12.2016
https://doi.org/10.18201/ijisae.265954

Öz

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.

Kaynakça

  • 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.
Toplam 12 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Research Article
Yazarlar

Yeşim Er

Ayten Atasoy

Yayımlanma Tarihi 26 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 4 Sayı: Special Issue-1

Kaynak Göster

APA Er, Y., & Atasoy, A. (2016). The Classification of White Wine and Red Wine According to Their Physicochemical Qualities. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 23-26. https://doi.org/10.18201/ijisae.265954
AMA Er Y, Atasoy A. The Classification of White Wine and Red Wine According to Their Physicochemical Qualities. International Journal of Intelligent Systems and Applications in Engineering. Aralık 2016;4(Special Issue-1):23-26. doi:10.18201/ijisae.265954
Chicago Er, Yeşim, ve Ayten Atasoy. “The Classification of White Wine and Red Wine According to Their Physicochemical Qualities”. International Journal of Intelligent Systems and Applications in Engineering 4, sy. Special Issue-1 (Aralık 2016): 23-26. https://doi.org/10.18201/ijisae.265954.
EndNote Er Y, Atasoy A (01 Aralık 2016) The Classification of White Wine and Red Wine According to Their Physicochemical Qualities. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 23–26.
IEEE Y. Er ve A. Atasoy, “The Classification of White Wine and Red Wine According to Their Physicochemical Qualities”, International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. Special Issue-1, ss. 23–26, 2016, doi: 10.18201/ijisae.265954.
ISNAD Er, Yeşim - Atasoy, Ayten. “The Classification of White Wine and Red Wine According to Their Physicochemical Qualities”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (Aralık 2016), 23-26. https://doi.org/10.18201/ijisae.265954.
JAMA Er Y, Atasoy A. The Classification of White Wine and Red Wine According to Their Physicochemical Qualities. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:23–26.
MLA Er, Yeşim ve Ayten Atasoy. “The Classification of White Wine and Red Wine According to Their Physicochemical Qualities”. International Journal of Intelligent Systems and Applications in Engineering, c. 4, sy. Special Issue-1, 2016, ss. 23-26, doi:10.18201/ijisae.265954.
Vancouver Er Y, Atasoy A. The Classification of White Wine and Red Wine According to Their Physicochemical Qualities. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):23-6.

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