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• W2097176879 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." @default.
• W2097176879 created "2016-06-24" @default.
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• W2097176879 date "2007-05-01" @default.
• W2097176879 modified "2023-10-05" @default.
• W2097176879 title "Consistent Feature Selection for Pattern Recognition in Polynomial Time" @default.
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