Streaming classification methods assume the number of input features is fixed
and always received. But in many real-world scenarios demand is some input
features are reliable while others are unreliable or inconsistent. In this
paper, we propose a novel deep learning-based model called Auxiliary Network
(Aux-Net), which is scalable and agile. It employs a weighted ensemble of
classifiers to give a final outcome. The Aux-Net model is based on the hedging
algorithm and online gradient descent. It employs a model of varying depth in
an online setting using single pass learning. Aux-Net is a foundational work
towards scalable neural network model for a dynamic complex environment
requiring ad hoc or inconsistent input data. The efficacy of Aux-Net is shown
on public dataset.

该研究提出了一种基于深度学习的模型 —— 辅助网络（Aux-Net），其采用加权集成分类器来处理输入特征数量的变化，具有可扩展性和敏捷性。Aux-Net 基于对冲算法和在线梯度下降算法，使用单次学习中的深度变化模型，适用于动态复杂环境，使能够处理不连贯的输入数据。在公共数据集上，实验证明了辅助网络的有效性。