Predicting how much a consumer is willing to pay for a bottle of wine: dealing with data imbalance

Abstract

The wine industry has becoming increasingly important worldwide and is one of the most significant industries in Portugal. In a previous paper, the problem of predicting how much a Portuguese consumer is willing to pay for a bottle of wine was considered for the first time ever. The problem was treated as a multi-class ordinal classification task. Although we achieved good prediction results, globally speaking, it was difficult to identify rare cases of consumers who are interested in paying for more expensive wines. We found that this was a direct consequence of data imbalance. Therefore, here, we present a first attempt to deal with this issue, based on the use of re-sampling strategies to balance the training data, namely random under-sampling, random over- sampling with replacement and the synthetic minority over-sampling technique. We consider several learning methods and develop various predictive models. A comparative study is carried out and its results highlight the importance of a careful choice of the re-sampling strategy and the learning method in order to get the best possible prediction results.