Consistent Feature Selection for Pattern Recognition in Polynomial Time

Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér.

Year: 2007, Volume: 8, Issue: 21, Pages: 589−612


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.