  Recent years have witnessed growing interests in online incremental learning.
However, there are three major challenges in this area. The first major
difficulty is concept drift, that is, the probability distribution in the
streaming data would change as the data arrives. The second major difficulty is
catastrophic forgetting, that is, forgetting what we have learned before when
learning new knowledge. The last one we often ignore is the learning of the
latent representation. Only good latent representation can improve the
prediction accuracy of the model. Our research builds on this observation and
attempts to overcome these difficulties. To this end, we propose an Adaptive
Online Incremental Learning for evolving data streams (AOIL). We use
auto-encoder with the memory module, on the one hand, we obtained the latent
features of the input, on the other hand, according to the reconstruction loss
of the auto-encoder with memory module, we could successfully detect the
existence of concept drift and trigger the update mechanism, adjust the model
parameters in time. In addition, we divide features, which are derived from the
activation of the hidden layers, into two parts, which are used to extract the
common and private features respectively. By means of this approach, the model
could learn the private features of the new coming instances, but do not forget
what we have learned in the past (shared features), which reduces the
occurrence of catastrophic forgetting. At the same time, to get the fusion
feature vector we use the self-attention mechanism to effectively fuse the
extracted features, which further improved the latent representation learning.
