软霍夫丁树：一种面向数据流的透明且可微模型
Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams
AI总结：
本文提出软霍夫丁树（SoHoT），通过新门控函数融合霍夫丁树的透明可扩展性与软树的可微性，在20个数据流上验证其在类别概率估计上优于霍夫丁树，并能用超参数权衡透明度与性能。
摘要：
我们提出软霍夫丁树（SoHoT），作为一种新型可微且透明的模型，适用于可能无限且不断变化的数据流。霍夫丁树等流挖掘算法根据传入的数据流进行生长，但目前缺乏端到端深度学习系统的适应性。如果特征表示由神经网络学习并用于树中，或者树的输出在深度学习模型或工作流中被进一步处理，端到端学习可能是可取的。与霍夫丁树不同，软树由于其可微性可以集成到此类系统中，但既不透明也不可解释。我们的新型模型结合了霍夫丁树的可扩展性和透明度与软树的可微性。我们引入了一种新的门控函数来调节树中单变量分裂和多变量分裂之间的平衡。在20个数据流上进行了实验，将SoHoT与标准霍夫丁树、复杂度受限的霍夫丁树以及应用稀疏激活函数进行样本路由的软树进行比较。结果表明，软霍夫丁树在估计类别概率方面优于霍夫丁树，同时与软树相比保持了透明度，在AUROC和交叉熵方面的损失相对较小。我们还演示了如何使用超参数在透明度和性能之间进行权衡，在谱的一端获得单变量分裂，在另一端获得多变量分裂。
英文摘要：
We propose soft Hoeffding trees (SoHoT) as a new differentiable and transparent model for possibly infinite and changing data streams. Stream mining algorithms such as Hoeffding trees grow based on the incoming data stream, but they currently lack the adaptability of end-to-end deep learning systems. End-to-end learning can be desirable if a feature representation is learned by a neural network and used in a tree, or if the outputs of trees are further processed in a deep learning model or workflow. Different from Hoeffding trees, soft trees can be integrated into such systems due to their differentiability, but are neither transparent nor explainable. Our novel model combines the extensibility and transparency of Hoeffding trees with the differentiability of soft trees. We introduce a new gating function to regulate the balance between univariate and multivariate splits in the tree. Experiments are performed on 20 data streams, comparing SoHoT to standard Hoeffding trees, Hoeffding trees with limited complexity, and soft trees applying a sparse activation function for sample routing. The results show that soft Hoeffding trees outperform Hoeffding trees in estimating class probabilities and, at the same time, maintain transparency compared to soft trees, with relatively small losses in terms of AUROC and cross-entropy. We also demonstrate how to trade off transparency against performance using a hyperparameter, obtaining univariate splits at one end of the spectrum and multivariate splits at the other.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
