  The concept of evolving intelligent system (EIS) provides an effective avenue
for data stream mining because it is capable of coping with two prominent
issues: online learning and rapidly changing environments. We note at least
three uncharted territories of existing EISs: data uncertainty, temporal system
dynamic, redundant data streams. This book chapter aims at delivering a
concrete solution of this problem with the algorithmic development of a novel
learning algorithm, namely PANFIS++. PANFIS++ is a generalized version of the
PANFIS by putting forward three important components: 1) An online active
learning scenario is developed to overcome redundant data streams. This module
allows to actively select data streams for the training process, thereby
expediting execution time and enhancing generalization performance, 2) PANFIS++
is built upon an interval type-2 fuzzy system environment, which incorporates
the so-called footprint of uncertainty. This component provides a degree of
tolerance for data uncertainty. 3) PANFIS++ is structured under a recurrent
network architecture with a self-feedback loop. This is meant to tackle the
temporal system dynamic. The efficacy of the PANFIS++ has been numerically
validated through numerous real-world and synthetic case studies, where it
delivers the highest predictive accuracy while retaining the lowest complexity.
