  Deep learning, a rebranding of deep neural network research works, has
achieved a remarkable success in recent years. With multiple hidden layers,
deep learning models aim at computing the hierarchical feature representations
of the observational data. Meanwhile, due to its severe disadvantages in data
consumption, computational resources, parameter tuning costs and the lack of
result explainability, deep learning has also suffered from lots of criticism.
In this paper, we will introduce a new representation learning model, namely
"Sample-Ensemble Genetic Evolutionary Network" (SEGEN), which can serve as an
alternative approach to deep learning models. Instead of building one single
deep model, based on a set of sampled sub-instances, SEGEN adopts a
genetic-evolutionary learning strategy to build a group of unit models
generations by generations. The unit models incorporated in SEGEN can be either
traditional machine learning models or the recent deep learning models with a
much "narrower" and "shallower" architecture. The learning results of each
instance at the final generation will be effectively combined from each unit
model via diffusive propagation and ensemble learning strategies. From the
computational perspective, SEGEN requires far less data, fewer computational
resources and parameter tuning efforts, but has sound theoretic
interpretability of the learning process and results. Extensive experiments
have been done on several different real-world benchmark datasets, and the
experimental results obtained by SEGEN have demonstrated its advantages over
the state-of-the-art representation learning models.
