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Open Access

Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics

Department of Computer Science, University of Central Arkansas, Conway, AR 72034, USA.
Department of Computer Science, University of Alabama, Tuscaloosa, AL 35487, USA.
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Abstract

Classifying wine according to their grade, price, and region of origin is a multi-label and multi-target problem in wineinformatics. Using wine reviews as the attributes, we compare several different multi-label/multi-target methods to the single-label method where each label is treated independently. We explore both single-label and multi-label approaches for a two-class problem for each of the labels and we explore both single-label and multi-target approaches for a four-class problem on two of the three labels, with the third label remaining a two-class problem. In terms of per-label accuracy, the single-label method has the best performance, although some multi-label methods approach the performance of single-label. However, multi-label/multi-target metrics approaches do exceed the performance of the single-label method.

Keywords

classificationinformaticsmachine learningmulti-labelmulti-targetsupport vector machineswinewineinformatics

References

[1]
Y. Er and A. Atasoy, The classification of white wine and red wine according to their physicochemical qualities, Int. J. Intell. Syst. Appl. Eng., vol. 4, pp. 23-26, 2016.
Crossref Google Scholar
[2]
P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, Modeling wine preferences by data mining from physicochemical properties, Decis. Support Syst., vol. 47, no. 4, pp. 547-553, 2009.
Crossref Google Scholar
[3]
S. E. Ebeler, Linking flavor chemistry to sensory analysis of wine, in Flavor Chemistry: Thirty Years of Progress, R. Teranishi, E. L. Wick, and I. Hornstein, eds. Boston, MA, USA: Springer, 1999, pp. 409-421.
Crossref
[4]
S. Chung, T. S. Park, S. H. Park, J. Y. Kim, S. Park, D. Son, Y. M. Bae, and S. I. Cho, Colorimetric sensor array for white wine tasting, Sensors, vol. 15, no. 8, pp. 18197-18208, 2015.
Crossref Google Scholar
[5]
J. Fu, C. Q. Huang, J. G. Xing, and J. B. Zheng, Pattern classification using an olfactory model with PCA feature selection in electronic noses: Study and application, Sensors, vol. 12, no. 3, pp. 2818-2830, 2012.
Crossref Google Scholar
[6]
B. Chen, C. Rhodes, A. Yu, and V. Velchev, The computational wine wheel 2.0 and the TriMax triclustering in wineinformatics, in Proc. 16th Industrial Conf. Data Mining, New York, NY, USA, 2016, pp. 223-238.
Crossref
[7]
B. Chen, V. Velchev, B. Nicholson, J. Garrison, M. Iwamura, and R. Battisto, Wineinformatics: Uncork Napa’s cabernet sauvignon by association rule based classification, in Proc. 2015 IEEE 14th Int. Conf. on Machine Learning and Applications, Miami, FL, USA, 2015, pp. 565-569.
Crossref
[8]
B. Chen, H. Le, C. Rhodes, and D. S. Che, Understanding the wine judges and evaluating the consistency through white-box classification algorithms, in Advances in Data Mining. Applications and Theoretical Aspects, P. Perner, ed. Springer, 2016, pp. 239-252.
Crossref
[9]
N. Wariishi, B. Flanagan, T. Suzuki, and S. Hirokawa, Sentiment analysis of wine aroma, in Proc. 2015 IIAI 4th Int. Congress on Advanced Applied Informatics, Okayama, Japan, 2015, pp. 207-212.
Crossref
[10]
B. Flanagan, N. Wariishi, T. Suzuki, and S. Hirokawa, Predicting and visualizing wine characteristics through analysis of tasting notes from viewpoints, in HCI International 2015-Posters’ Extended Abstracts, C. Stephanidis, ed. Springer, 2015, pp. 613-619.
Crossref
[11]
Wine Spectator, About our tastings, http://www.winespectator.com/display/show/id/about-our-tastings, 2018.
[12]
B. Chen, C. Rhodes, A. Crawford, and L. Hambuchen, Wineinformatics: Applying data mining on wine sensory reviews processed by the computational wine wheel, in Proc. 2014 IEEE Int. Conf. on Data Mining Workshop, Shenzhen, China, 2014, pp. 142-149.
Crossref
[13]
E. Spyromitros-Xioufis, W. Groves, G. Tsoumakas, and I. Vlahavas, Multi-label classification methods for multi-target regression, arXiv preprint arXiv: 1211.6581, 2012.
Google Scholar
[14]
Wine Spectator, Wine Spectator’s 100-point scale, http://www.winespectator.com/display/show/id/scoring-scale, 2018.
[15]
K. Anderson, The World’s Wine Markets: Globalization at Work. Cheltenham, England: Edward Elgar, 2004.
Crossref
[16]
C. A. Tawiah and V. S. Sheng, Empirical comparison of multi-label classification algorithms, in Proc. 27th AAAI Conf. on Artificial Intelligence, Bellevue, WA, USA, 2013, pp. 2-6.
Crossref
[17]
G. Tsoumakas and I. Katakis, Multi-label classification: An overview, in Database Technologies: Concepts, Methodologies, Tools, and Applications, J. Erickson, ed. Barcelona, Spain: IGI Global, 2009, pp. 4-6, 10-12.
[18]
J. Read, B. Pfahringer, G. Holmes, and E. Frank, Classifier chains for multi-label classification, Mach. Learn., vol. 85, no. 3, pp. 333-359, 2011.
Crossref Google Scholar
[19]
J. H. Zaragoza, L. E. Sucar, E. F. Morales, C. Bielza, and P. Larrañaga, Bayesian chain classifiers for multidimensional classification, in Proc. 22nd Int. Joint Conf. on Artificial Intelligence, Barcelona, Spain, 2011, pp. 2192-2197.
[20]
J. Read, L. Martino, P. M. Olmos, and D. Luengo, Scalable multi-output label prediction: From classifier chains to classifier trellises, Pattern Recognition, vol. 48, no. 6, pp. 2096-2109, 2015.
Crossref Google Scholar
[21]
Y. H. Guo and S. C. Gu, Multi-label classification using conditional dependency networks, in Proc. 22nd Int. Joint Conf. on Artificial Intelligence, Barcelona, Spain, 2011, pp. 1300-1305.
[22]
J. Read, Multi-label classification, https://jmread.github.io/talks/Tutorial-MLC-Porto.pdf, 2015.
Crossref
[23]
D. Fradkin and I. Muchnik, Support vector machines for classification, DIMACS Series in Discrete Mathematics and Theorectical Computer Science, vol. 70, pp. 13-20, 2006.
Google Scholar
[24]
B. Baesens, T. van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen, Benchmarking state-of-the-art classification algorithms for credit scoring, J. Oper. Res. Soc., vol. 54, no. 6, pp. 627-635, 2003.
Crossref Google Scholar
[25]
E. Byvatov, U. Fechner, J. Sadowski, and G. Schneider, Comparison of support vector machine and artificial neural network systems for drug/nondrug classification, J. Chem. Inf. Comput. Sci., vol. 43, no. 6, pp. 1882-1889, 2003.
Crossref Google Scholar
[26]
H. Yoon, S. C. Jun, Y. Hyun, G. O. Bae, and K. K. Lee, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J. Hydrol., vol. 396, nos. 1&2, pp. 128-138, 2011.
Crossref Google Scholar
[27]
O. Chapelle, P. Haffner, and V. N. Vapnik, Support vector machines for histogram-based image classification, IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1055-1064, 1999.
Crossref Google Scholar
[28]
C. C. Chang and C. J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, p. 27, 2011.
Crossref Google Scholar
[29]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, The WEKA data mining software: An update, ACM SIGKDD Explor. Newslett., vol. 11, no. 1, pp. 10-18, 2009.
Crossref Google Scholar
[30]
J. Read, P. Reutemann, B. Pfahringer, and G. Holmes, MEKA: A multi-label/multi-target extension to Weka, J. Mach. Learn. Res., vol. 17, no. 1, pp. 667-671, 2016.
Google Scholar
Big Data Mining and Analytics
Volume 3 Issue 1,
March 2020
Pages 1-12
DOI: 10.26599/BDMA.2019.9020014
Cite this article:
Palmer J, Sheng VS, Atkison T, et al. Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics. Big Data Mining and Analytics, 2020, 3(1): 1-12. https://doi.org/10.26599/BDMA.2019.9020014

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Received: 02 July 2019
Accepted: 05 September 2019
Published: 19 December 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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