Title: Deep Stacked Stochastic Configuration Networks for Non-Stationary Data Streams,
Abstract: The concept of stochastic configuration networks (SCNs) others a solid
framework for fast implementation of feedforward neural networks through
randomized learning. Unlike conventional randomized approaches, SCNs provide an
avenue to select appropriate scope of random parameters to ensure the universal
approximation property. In this paper, a deep version of stochastic
configuration networks, namely deep stacked stochastic configuration network
(DSSCN), is proposed for modeling non-stationary data streams. As an extension
of evolving stochastic connfiguration networks (eSCNs), this work contributes a
way to grow and shrink the structure of deep stochastic configuration networks
autonomously from data streams. The performance of DSSCN is evaluated by six
benchmark datasets. Simulation results, compared with prominent data stream
algorithms, show that the proposed method is capable of achieving comparable
accuracy and evolving compact and parsimonious deep stacked network
architecture.