Aggregation tools for the evaluation of classifications
Fabián Castiblanco, Daniel Gómez, Javier Montero, J. Tinguaro Rodríguez · 2017
This paper focuses on key issues related to aggregation tools within knowledge acquisition, and in particular for image processing. In particular, it is claimed the need of a number of simultaneous indices in order to evaluate the quality of a fuzzy classification (e.g., overlapping, covering and relevance), and that such a family of indices should allow learning, giving a hint on how such a classification can be improved. Our guess is that those indices can be obtained from certain families of well-known aggregation tools.