Machine Collaboration
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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.
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