Scientists must understand what machines do
(systems should not behave like a black box), because in
many cases how they predict is more important than what
they predict. In this work, we propose a new extension of
the fuzzy linguistic grammar and a mainly novel interpretable
linear extension for regression problems, together
with an enhanced new linguistic tree-based evolutionary
multiobjective learning approach. This allows the general
behavior of the data covered, as well as their specific
variability, to be expressed as a single rule. In order to
ensure the highest transparency and accuracy values, this
learning process maximizes two widely accepted semantic
metrics and also minimizes both the number of rules and
the model mean squared error. The results obtained in 23
regression datasets show the effectiveness of the proposed
method by applying statistical tests to the said metrics,
which cover the different aspects of the interpretability of
linguistic fuzzy models. This learning process has obtained
the preservation of high-level semantics and less than 5
rules on average, while it still clearly outperforms some of
the previous state-of-the-art linguistic fuzzy regression
methods for learning interpretable regression linguistic
fuzzy systems, and even to a competitive, pure accuracyoriented
linguistic learning approach. Finally, we analyze a
case study in a real problem related to childhood obesity,
and a real expert carries out the analysis shown.Andalusian Government P18-RT-2248Health Institute Carlos III/Spanish Ministry of Science, Innovation and Universities PI20/00711Spanish Government PID2019-107793GB-I00
PID2020-119478GB-I0
