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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Wine Spectator, About our tastings, , 2018.
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.
E. Spyromitros-Xioufis, W. Groves, G. Tsoumakas, and I. Vlahavas, Multi-label classification methods for multi-target regression, arXiv preprint arXiv: 1211.6581, 2012.
Wine Spectator, Wine Spectator’s 100-point scale, , 2018.
K. Anderson, The World’s Wine Markets: Globalization at Work. Cheltenham, England: Edward Elgar, 2004.
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.
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.
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.
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.
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.
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.
J. Read, Multi-label classification, , 2015.
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.
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.
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.
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.
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.
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.
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.
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.