1. Prediction of Wine Grade
ECE 539: Intro to Artificial Neural Networks
Matthew Kean

2. Wine Consumable product
Various physicochemical properties that contribute to the flavor of it (i.e. ABV, pH, sugar content, etc.)
Large market with significant competition to produce the best product

3. Goal of this Work
Classification problem: determine the grade of a particular sample based only on the physicochemical properties
Use a MLP to get a better classification rate than KNN
If successful, the same method could be used for other consumable products

4. Data Collection
Two different types of wine: one red and one white from a Portuguese vineyard
Retrieved from UC-Ivine database (
6000 samples total
11 features for each sample, with none missing

5. KNN
Used a KNN classifier to determine the benchmark for a multi-layer perceptron classifier
It had a peak value of 55% and an average classifying rate of 43%

6. Results for the MLP
So far I have only been able to train MLPs with accuracies averaging 44%
No advantage over the KNN yet

7. Other Work
By reducing the number of classes, a classification rate of over 90% was achieved
A different group obtained an 85% classification rate using a MLP, but they also got a slightly better classification rate using support vector machines.