Discovering relevancies in very difficult regression problems: applications to sensory data analysis

Abstract

Learning preferences is a useful tool in application fields like information retrieval, or system configuration. In this paper we show a new application of this Machine Learning tool, the analysis of sensory data provided by consumer panels. These data sets collect the ratings given by a set of consumers to the quality or the acceptability of market products that are principally appreciated through sensory impressions. The aim is to improve the production processes of food industries. We show how these data sets can not be processed in a useful way by regression methods, since these methods can not deal with some subtleties implicit in the available knowledge. Using a collection of real world data sets, we illustrate the benefits of our approach, showing that it is possible to obtain useful models to explain the behavior of consumers where regression methods only predict a constant reaction in all consumers, what is useless and unacceptable