R.: Active algorithm selection

Abstract Most previous studies on active learning focused on the problem of model selection, i.e., how to identify the optimal classification model from a family of predefined models using a small, carefully selected training set. In this paper, we address the problem of active algorithm selection. The goal of this problem is to efficiently identify the optimal learning algorithm for a given dataset from a set of algorithms using a small training set. In this study, we present a general framework for active algorithm selection by extending the idea of the Hedge algorithm. It employs the worst case analysis to identify the example that can effectively increase the weighted loss function defined in the Hedge algorithm. We further extend the framework by incorporating the correlation information among unlabeled examples to accurately estimate the change in the weighted loss function, and Maximum Entropy Discrimination to automatically determine the combination weights used by the Hedge algorithm. Our empirical study with the datasets of WCCI 2006 performance prediction challenge shows promising performance of the proposed framework for active algorithm selection