International Journal of Internet, Broadcasting and Communication

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Wine Quality Classification with Multilayer Perceptron

  • Agrawal, Garima (Department of Ubiquitous Computing, Dongseo University) ;
  • Kang, Dae-Ki (Department of Ubiquitous Computing, Dongseo University)
  • Received : 2018.02.20
  • Accepted : 2018.03.15
  • Published : 2018.05.31
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Abstract

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.

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References

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