This paper proposes a new approach to ensemble learning that combines multiple supervised and unsupervised models. The approach formulates the ensemble task as an optimization problem on a bipartite graph. The objective is to maximize consensus among the supervised predictions and unsupervised constraints by favoring smooth predictions over the graph while penalizing deviations from the initial supervised labels. This is solved through iterative propagation of probability estimates among neighboring nodes on the graph. Experimental results demonstrate the benefits of this new approach over existing alternatives.

1. A GRAPH-BASED CONSENSUS MAXIMIZATION APPROACH FOR COMBINING
MULTIPLE SUPERVISED AND UNSUPERVISED MODELS
ABSTRACT:
Ensemble learning has emerged as a powerful method for combining multiple models. Well-
known methods, such as bagging, boosting, and model averaging, have been shown to improve
accuracy and robustness over single models. However, due to the high costs of manual labeling,
it is hard to obtain sufficient and reliable labeled data for effective training. Meanwhile, lots of
unlabeled data exist in these sources, and we can readily obtain multiple unsupervised models.
Although unsupervised models do not directly generate a class label prediction for each object,
they provide useful constraints on the joint predictions for a set of related objects. Therefore,
incorporating these unsupervised models into the ensemble of supervised models can lead to
better prediction performance.
In this paper, we study ensemble learning with outputs from multiple supervised and
unsupervised models, a topic where little work has been done. We propose to consolidate a
classification solution by maximizing the consensus among both supervised predictions and
unsupervised constraints. We cast this ensemble task as an optimization problem on a bipartite
graph, where the objective function favors the smoothness of the predictions over the graph, but
penalizes the deviations from the initial labeling provided by the supervised models. We solve
this problem through iterative propagation of probability estimates among neighboring nodes and
prove the optimality of the solution. The proposed method can be interpreted as conducting a
constrained embedding in a transformed space, or a ranking on the graph. Experimental results
on different applications with heterogeneous data sources demonstrate the benefits of the
proposed method over existing alternatives.
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