2021/05/06 by Yan Feng, Liu, Qingfeng, Feng, Yang
Computer Science · #62G05 #62G08 #62J02 #62J07 #62J12 #68Q32 #68T05 #68T07 #Domain Adaptation and Few-Shot Learning #Econometrics (econ.EM) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Economics and business #G.3 #I.2.6 #I.6.4 #I.6.5 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2105.02569
openalex publication_date 2021/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of base machines for prediction tasks. Unlike bagging/stacking (a parallel & independent framework) and boosting (a sequential & top-down framework), MaC is a type of circular & interactive learning framework. The circular & interactive feature 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 & interactive 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.