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Anti-Learning

Anti-Learning

This video was recorded at Machine Learning Summer School (MLSS), Canberra 2006. The Biological domain poses new challenges for statistical learning. In the talk we shall analyze and theoretically explain some counter-intuitive experimental and theoretical findings that systematic reversal of classifier decisions can occur when switching from training to independent test data (the phenomenon of anti-learning). We demonstrate this on both natural and synthetic data and show that it is distinct from overfitting. The natural datasets discussed will include: prediction of response to chemo-radio-therapy for esophageal cancer from gene expression (measured by cDNA-microarrays); prediction of genes affecting the aryl hydrocarbon receptor pathway in yeast. The main synthetic classification problem will be the approximation of samples drawn from high dimensional distributions, for which a theoretical explanation will be outlined.

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