A bootstrapping method for learning from heterogeneous data

Abstract

In machine learning applications where multiple data sources present, it is desirable to effectively exploit the sources simultaneously to make better inferences. When each data source is presented as a graph, a common strategy is to combine the graphs, e.g. by taking the sum of their adjacency matrices, and then apply standard graph-based learning algorithms. In this paper, we take an alternative approach to this problem. Instead of performing the combination step, a graph-based learner is created on each graph and makes predictions independently. The method works in an iterative manner: labels predicted by some learners in each round are added to the labeled set and the models are retrained. By nature, the method is based on two popular semi-supervised learning approaches: bootstrapping and graph-based methods, to take their advantages. We evaluated the method on the gene function prediction problem with real biological datasets. Experiments show that our method significantly outperforms a standard graph-based algorithm and compares favorably with a state-of-the-art gene function prediction method. © 2011 Springer-Verlag.