Mercredi 4 Juillet

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Mercredi 4 Juillet
Heure: 14:00 - 15:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Cost Efficient Prediction of Wine Quality - A Machine Learning Approach
Description: Razvan Andonie The quality of wines can be assessed both from chemical/biological tests and sensory tests (which rely mainly on human experts). Determining which is the subset of tests to be used is a difficult problem. Each test has its own contribution for predicting the quality of wines and, in addition, its own cost. We use our own database, consisting of 32 wine characteristics applied to 180 wine samples. In addition we use wine quality labels assigned by a wine expert. To the extent of our knowledge, this is the first study of this kind on wines from Washington State, and also the first wine study in general to include cost minimization of the measurements as a goal. Our approach is based on two stages. First, we identify reasonably good classifiers (from a given set of classifiers). Next, we search for the optimal subset of features to maximize the performance of the best classifier and also minimize the overall cost of the measurements. As a result, through our method we can answer queries like ``the best performing subset of tests for a given threshold cost’'.