Multiple Comparisons in Induction Algorithms
Machine Learning
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Rule-based anomaly pattern detection for detecting disease outbreaks
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An introduction to variable and feature selection
The Journal of Machine Learning Research
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A new feature selection algorithm based on binomial hypothesis testing for spam filtering
Knowledge-Based Systems
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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.