The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

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 criterion for sparse, high-dimensional problems. We evaluate this method on 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.

Keywords

order statistics, extreme value, feature selection, multiple hypothesis testing

Notes

Text Reference

Mark Palatucci and Andrew Carlson, "On the Chance Accuracies of Large Collections of Classifiers," Proceedings of the 25th International Conference on Machine Learning, July, 2008.

BibTeX Reference

@inproceedings{Palatucci_2008_6049,
   author = "Mark Palatucci and Andrew Carlson",
   title = "On the Chance Accuracies of Large Collections of Classifiers",
   booktitle = "Proceedings of the 25th International Conference on Machine Learning",
   month = "July",
   year = "2008",
}

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