From Ensemble Methods to Comprehensible Models

Ensemble methods improve accuracy by combining the predictionsof a set of dierent hypotheses. However, there are two importantshortcomings associated with ensemble methods. Huge amounts ofmemory are required to store a set of multiple hypotheses and, more importantly,comprehensibility of a single hypothesis is lost. In this work,we devise a new method to extract one single solution from a hypothesisensemble without using extra data, based on two main ideas: the selectedsolution...