Abstract

Research on classifier transferability intends that the information gathered in the solution of a given classification problem could be reused in the solution of similar or related problems. We propose the evolution of transferable classifiers based on the use of multi-objective genetic programming and new fitness-functions that evaluate the amount of transferability. We focus on the domain adaptation scenario in which the problem to be solved is the same in the source and target domains, but the distribution of data is different between domains. As a real-world test case we address the brain decoding problem, whose goal is to predict the stimulus presented to a subject from the analysis of his brain activity. Brain decoding across subjects attempts to reuse the classifiers learned from some subjects in the classification of the others. We evolved GP-based classifiers using different variants of the introduced approach to test their effectiveness on data obtained from a brain decoding experiment involving 16 subjects. Our results show that the GP-based classifiers evolved trying to maximize transferability are able to improve classification accuracy over other classical classifiers that incorporate domain adaptation methods. Moreover, after comparing our algorithm to importance-weighted cross validation (in conjunction with many ML methods), we conclude that our approach achieves state of the art results in terms of transferability.