Beam Search Extraction and Forgetting Strategies on Shared Ensembles

Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. However, there is an important shortcoming associated with ensemble methods. Huge amounts of memory are required to store a set of multiple hypotheses. In this work we devise an ensemble method that partially solves this drawbacks. The key point is that components share their common parts. For this goal, we employ a multi-tree, a structure that can simultaneously contain an ensemble of decision trees, but with the advantage that decision trees share some conditions. To construct this multi-tree we define an algorithm based on a beam search with several extraction criteria and with several forgetting policies for the suspended nodes. Finally, we compare the behaviour of this ensemble method with some well-known methods for generating hypothesis ensembles.