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The concept of evolving intelligent system (EIS) provides an effective avenuefor data stream mining because it is capable of coping with two prominentissues: online learning and rapidly changing environments. We note at leastthree uncharted territories of existing EISs: data uncertainty, temporal systemdynamic, redundant data streams. This book chapter aims at delivering aconcrete solution of this problem with the algorithmic development of a novellearning algorithm, namely PANFIS++. PANFIS++ is a generalized version of thePANFIS by putting forward three important components: 1) An online activelearning scenario is developed to overcome redundant data streams. This moduleallows to actively select data streams for the training process, therebyexpediting execution time and enhancing generalization performance, 2) PANFIS++is built upon an interval type-2 fuzzy system environment, which incorporatesthe so-called footprint of uncertainty. This component provides a degree oftolerance for data uncertainty. 3) PANFIS++ is structured under a recurrentnetwork architecture with a self-feedback loop. This is meant to tackle thetemporal system dynamic. The efficacy of the PANFIS++ has been numericallyvalidated through numerous real-world and synthetic case studies, where itdelivers the highest predictive accuracy while retaining the lowest complexity.

PANFIS++: A Generalized Approach to Evolving Learning