  Multi-label classification has attracted much attention in the machine
learning community to address the problem of assigning single samples to more
than one class at the same time. We propose an evolving multi-label fuzzy
classifier (EFC-ML) which is able to self-adapt and self-evolve its structure
with new incoming multi-label samples in an incremental, single-pass manner. It
is based on a multi-output Takagi-Sugeno type architecture, where for each
class a separate consequent hyper-plane is defined. The learning procedure
embeds a locally weighted incremental correlation-based algorithm combined with
(conventional) recursive fuzzily weighted least squares and Lasso-based
regularization. The correlation-based part ensures that the interrelations
between class labels, a specific well-known property in multi-label
classification for improved performance, are preserved properly; the
Lasso-based regularization reduces the curse of dimensionality effects in the
case of a higher number of inputs. Antecedent learning is achieved by
product-space clustering and conducted for all class labels together, which
yields a single rule base, allowing a compact knowledge view. Furthermore, our
approach comes with an online active learning (AL) strategy for updating the
classifier on just a number of selected samples, which in turn makes the
approach applicable for scarcely labelled streams in applications, where the
annotation effort is typically expensive. Our approach was evaluated on several
data sets from the MULAN repository and showed significantly improved
classification accuracy compared to (evolving) one-versus-rest or classifier
chaining concepts. A significant result was that, due to the online AL method,
a 90\% reduction in the number of samples used for classifier updates had
little effect on the accumulated accuracy trend lines compared to a full update
in most data set cases.
