Abstract As an ensemble algorithm, network boosting enjoys a powerful classification ability but suffers from the tedious and time-consuming training process. To tackle the problem, in this paper, a broad network gradient boosting system (BNGBS) is developed by integrating gradient boosting machine with broad networks, in which the classification loss caused by a base broad network is learned and eliminated by followed networks in a cascade manner. The proposed system is constructed as an additive model and can be easily optimized by a greedy strategy instead of the tedious back-propagation algorithm, resulting in a more efficient learning process. Meanwhile, triple incremental learning capabilities including the increment of feature nodes, increment of input samples, and increment of target classes are designed. The proposed system can be efficiently updated and expanded based on the current status instead of being entirely retrained when the demands for more feature nodes, input samples, and target classes are proposed. The node-increment ability allows to add more feature nodes into the built system if the current structures are not effective for learning. The sample-increment ability is developed to allow the model to keep learning from the coming batch data. The class-increment ability is used to tackle the issue that the coming batch data may contain unseen categories. In comparison with existing popular machine learning methods, comprehensive results based on eight benchmark datasets illustrate the effectiveness of the proposed broad network gradient boosting system for the classification task.

中文翻译：

---

BNGBS：一个高效的网络提升系统，具有更多节点、样本和类别的三重增量学习能力

摘要 作为一种集成算法，网络提升具有强大的分类能力，但其训练过程繁琐耗时。为了解决这个问题，在本文中，通过将梯度提升机与宽网络相结合，开发了一种宽网络梯度提升系统（BGBBS），其中由基础宽网络引起的分类损失被级联的后续网络学习和消除。方式。所提出的系统被构造为加法模型，并且可以通过贪婪策略而不是繁琐的反向传播算法轻松进行优化，从而提高学习过程的效率。同时设计了特征节点增量、输入样本增量、目标类增量三重增量学习能力。当提出对更多特征节点、输入样本和目标类的需求时，可以根据当前状态有效地更新和扩展所提出的系统，而不是完全重新训练。如果当前结构对学习无效，则节点增量能力允许将更多特征节点添加到构建的系统中。开发了样本增量能力，以允许模型不断从即将到来的批次数据中学习。类增量能力用于解决即将到来的批次数据可能包含未见类别的问题。与现有流行的机器学习方法相比，基于八个基准数据集的综合结果说明了所提出的广泛网络梯度提升系统对分类任务的有效性。