Abstract
In this study, deep neural networks are developed to evaluate its performance over wine data set from UCI repository. The data set consists of white and red wine samples from Portugal. Previous studies claimed that Support Vector Machine (SVM) outperformed the simple ANN and Multiple Regression (MR) on wine data set. We trained different neural networks models with different hidden layers and activations to understand if it is possible to achieve better accuracy. It is found that deep learning approach is able to provide better prediction accuracy than SVM even on a smaller data set.
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Acknowledgements
This work was financially supported by the Ministry of Education and Science of Russian Federation (government order 2.7905.2017/8.9).
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Kumar, S., Kraeva, Y., Kraleva, R., Zymbler, M. (2020). A Deep Neural Network Approach to Predict the Wine Taste Preferences. In: Solanki, V., Hoang, M., Lu, Z., Pattnaik, P. (eds) Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1125. Springer, Singapore. https://doi.org/10.1007/978-981-15-2780-7_120
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DOI: https://doi.org/10.1007/978-981-15-2780-7_120
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