We propose a new ensemble framework for supervised learning, named machine collaboration (MaC), based on a collection of base machines for prediction tasks. Different from 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 own structures and parameters accordingly. The theoretical result on the risk bound of the estimator based on MaC shows that circular & interactive feature can help MaC reduce the risk via a parsimonious ensemble. We conduct extensive experiments on  simulated data and 119 benchmark real data sets . The results of the experiments show that in most cases, MaC performs much better than several  state-of-the-art methods, including CART, neural network , stacking, and boosting.
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.
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