Machine collaboration
Machine collaboration
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DOI：
10.1002/sta4.661
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发表时间：
2021-05
期刊：
影响因子：
1.7
通讯作者：
Qingfeng Liu;Yang Feng
中科院分区：
文献类型：
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作者：
Qingfeng Liu;Yang Feng
我们提出了一种新的监督学习集成框架，称为机器协作(Mac)，使用一组可能不同的基本学习方法(以下称为基本机器)来执行预测任务。与装袋/堆叠(一个并行且独立的框架)和Boosting(一个顺序且自上而下的框架)不同，Mac是一种循环和递归的学习框架。循环和递归的性质有助于基础机器循环传递信息，并相应地更新其结构和参数。对Mac估计的风险界的理论结果表明，循环和递归特征可以帮助Mac通过节俭的集成来降低风险。我们使用模拟数据和119个基准真实数据集在Mac上进行了广泛的实验。结果表明，在大多数情况下，Mac的性能明显好于其他几种最先进的方法，包括分类和回归树、神经网络、堆叠和增强。
We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of possibly heterogeneous base learning methods (hereafter, base machines) for prediction tasks. Unlike bagging/stacking (a parallel and independent framework) and boosting (a sequential and top‐down framework), MaC is a type of circular and recursive learning framework. The circular and recursive nature helps the base machines to transfer information circularly and update their structures and parameters accordingly. The theoretical result on the risk bound of the estimator from MaC reveals that the circular and recursive feature can help MaC reduce risk via a parsimonious ensemble. We conduct extensive experiments on MaC using both simulated data and 119 benchmark real datasets. The results demonstrate that in most cases, MaC performs significantly better than several other state‐of‐the‐art methods, including classification and regression trees, neural networks, stacking, and boosting.