Vocabulary elicitation for informative descriptions of classes
Gregory J. Smits, Olivier Pivert, Marie‐Jeanne Lesot · 2017
Linguistic descriptions of numerical data using a vocabulary defined as linguistic variables are particularly useful to help a user understand the content of a dataset. When dealing with data structured with classes, the relevance of the linguistic descriptions strongly relies on the adequacy between the vocabulary and this data structure. This paper proposes a criterion to quantify this relevance, understood as informativeness and measured in terms of specificity. It then proposes various strategies to elicit appropriate fuzzy partitions to define the modalities of relevant linguistic variables and it experimentally examines their performance on artificial data sets.