Consistent Feature Selection for Pattern Recognition in Polynomial Time
| Citations: | 3 - 0 self |
BibTeX
@MISC{Nilsson_consistentfeature,
author = {Roland Nilsson and José M. Peña and Johan Björkegren and Jesper Tegnér and Leon Bottou},
title = {Consistent Feature Selection for Pattern Recognition in Polynomial Time},
year = {}
}
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Abstract
We analyze two different feature selection problems: finding a minimal feature set optimal for classification (MINIMAL-OPTIMAL) vs. finding all features relevant to the target variable (ALL-RELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of data distributions for which there exist consistent, polynomial-time algorithms. We also prove that ALL-RELEVANT is much harder than MINIMAL-OPTIMAL and propose two consistent, polynomial-time algorithms. We argue that the distribution classes considered are reasonable in many practical cases, so that our results simplify feature selection in a wide range of machine learning tasks.
