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On the Chance Accuracies of Large Collections of Classifiers

On the Chance Accuracies of Large Collections of Classifiers

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. We provide a theoretical analysis of the chance accuracies of large collections of classifiers. We show that on problems with small numbers of examples, some classifier can perform well by random chance, and we derive a theorem to explicitly calculate this accuracy. We use this theorem to provide a principled feature selection criteria for sparse, high-dimensional problems. We evaluate this method on both microarray and fMRI datasets and show that it performs very close to the optimal accuracy obtained from an oracle. We also show that on the fMRI dataset this technique chooses relevant features successfully while another state-of-the-art method, the False Discovery Rate (FDR), completely fails at standard significance levels.


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