Modeling wine preferences by data mining from physicochemical properties Export

@article{citeulike:5312587, abstract = {We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.}, author = {Cortez, Paulo and Cerdeira, Ant\'{o}nio and Almeida, Fernando and Matos, Telmo and Reis, Jos\'{e}}, citeulike-article-id = {5312587}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.dss.2009.05.016}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0167923609001377}, day = {09}, doi = {10.1016/j.dss.2009.05.016}, issn = {01679236}, journal = {Decision Support Systems}, keywords = {model, model\_selection, preference, regression, svm, thorough\_parameter\_selection, wine}, month = {June}, posted-at = {2009-10-22 10:33:53}, priority = {0}, title = {Modeling wine preferences by data mining from physicochemical properties}, url = {http://dx.doi.org/10.1016/j.dss.2009.05.016}, year = {2009} }

TY - JOUR ID - citeulike:5312587 L3 - citeulike-article-id:5312587 N2 - We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets. JF - Decision Support Systems SN - 01679236 TI - Modeling wine preferences by data mining from physicochemical properties KW - model KW - model_selection KW - preference KW - regression KW - svm KW - thorough_parameter_selection KW - wine AU - Cortez, Paulo AU - Cerdeira, António AU - Almeida, Fernando AU - Matos, Telmo AU - Reis, José PY - 2009/06/09/ UR - http://dx.doi.org/10.1016/j.dss.2009.05.016 ER -