  This paper proposes a new architecture of incremen-tal fuzzy inference system
(also called Evolving Fuzzy System-EFS). In the context of classifying data
stream in non stationary environment, concept drifts problems must be
addressed. Several studies have shown that EFS can deal with such environment
thanks to their high structural flexibility. These EFS perform well with smooth
drift (or incremental drift). The new architecture we propose is focused on
improving the processing of brutal changes in the data distribution (often
called brutal concept drift). More precisely, a generalized EFS is paired with
a module of anticipation to improve the adaptation of new rules after a brutal
drift. The proposed architecture is evaluated on three datasets from UCI
repository where artificial brutal drifts have been applied. A fit model is
also proposed to get a "reactivity time" needed to converge to the steady-state
and the score at end. Both characteristics are compared between the same system
with and without anticipation and with a similar EFS from state-of-the-art. The
experiments demonstrates improvements in both cases.
