Machine Collaboration
This work addresses the need for more effective ensemble methods in machine learning, offering a novel framework that could enhance predictive performance across various tasks, though it appears incremental as it builds on existing ensemble techniques like bagging and boosting.
The authors tackled the problem of improving prediction accuracy in supervised learning by proposing a new ensemble framework called machine collaboration (MaC), which uses a circular and interactive approach to allow base machines to transfer information and update parameters, resulting in MaC outperforming state-of-the-art methods like classification and regression trees, neural networks, stacking, and boosting on most of 119 benchmark datasets.
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.