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Big Data Mining and Analytics 2020, 3(1): 1-12 https://doi.org/10.26599/BDMA.2019.9020014
Open Access | Issue | Published: 19 December 2019

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

Show Author's Information James Palmer,  Victor S. Sheng,  Travis Atkison,  Bernard Chen( )
Department of Computer Science, University of Central Arkansas, Conway, AR 72034, USA.
Department of Computer Science, University of Alabama, Tuscaloosa, AL 35487, USA.
Keywords:
classification, machine learning, multi-label, informatics, multi-target, support vector machines, wine, wineinformatics
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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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Abstract Full text About this article

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.


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

Show Author's information James Palmer, Victor S. Sheng, Travis Atkison, Bernard Chen( )
Department of Computer Science, University of Central Arkansas, Conway, AR 72034, USA.
Department of Computer Science, University of Alabama, Tuscaloosa, AL 35487, USA.

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: classification, machine learning, multi-label, informatics, multi-target, support vector machines, wine, wineinformatics

References(30)

[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.
DOI 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.
DOI 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.
DOI
[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.
DOI 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.
DOI 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.
DOI
[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.
DOI
[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.
DOI
[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.
DOI
[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.
DOI
[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.
DOI
[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.
DOI
[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.
DOI
[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.
DOI 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.
DOI 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.
DOI
[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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI 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.
DOI 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
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Received: 02 July 2019
Accepted: 05 September 2019
Published: 19 December 2019
Issue date: March 2020

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