The large-scale, scalable, flexible characteristics make the researches of online update more important in the Internet of Things (IoT). Gradient Boosting Decision Tree (GBDT) is commonly used to deal with the numerical data, while it cannot be updated online. To better solve the prediction problems, we propose a more generalized approach, M-GBDT2NN, based on GBDT2NN. Compared with GBDT2NN, the new method is also applicable to the multi-classification problems, besides binary classification, regression. In the new framework, it takes the iteration of GBDT as the smallest unit to ensure the additive relation among the distilled models, and it predicts a probability vector rather than a numerical value. This paper analyzes the generalization and the ability of online update of M-GBDT2NN. The experimental results demonstrate that the proposed method can perform better than the other methods in both multi-classification problems and online update.

大规模，可扩展，灵活的特性使在线更新的研究在物联网（IoT）中变得更加重要。梯度提升决策树（GBDT）通常用于处理数字数据，但无法在线更新。为了更好地解决预测问题，我们在GBDT2NN的基础上提出了一种更通用的方法M-GBDT2NN。与GBDT2NN相比，该新方法除二进制分类，回归分析外，还适用于多分类问题。在新框架中，它以GBDT的迭代为最小单位来确保提炼模型之间的可加关系，并且它预测的是概率矢量而不是数值。本文分析了M-GBDT2NN的一般性和在线更新的能力。