  The generative learning phase of Autoencoder (AE) and its successor Denosing
Autoencoder (DAE) enhances the flexibility of data stream method in exploiting
unlabelled samples. Nonetheless, the feasibility of DAE for data stream
analytic deserves in-depth study because it characterizes a fixed network
capacity which cannot adapt to rapidly changing environments. An automated
construction of a denoising autoeconder, namely deep evolving denoising
autoencoder (DEVDAN), is proposed in this paper. DEVDAN features an open
structure both in the generative phase and in the discriminative phase where
input features can be automatically added and discarded on the fly. A network
significance (NS) method is formulated in this paper and is derived from the
bias-variance concept. This method is capable of estimating the statistical
contribution of the network structure and its hidden units which precursors an
ideal state to add or prune input features. Furthermore, DEVDAN is free of the
problem- specific threshold and works fully in the single-pass learning
fashion. The efficacy of DEVDAN is numerically validated using nine
non-stationary data stream problems simulated under the prequential
test-then-train protocol where DEVDAN is capable of delivering an improvement
of classification accuracy to recently published online learning works while
having flexibility in the automatic extraction of robust input features and in
adapting to rapidly changing environments.
