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An Aggregation Method for Sparse Logistic Regression
[article]
2015
arXiv
pre-print
L_1 regularized logistic regression has now become a workhorse of data mining and bioinformatics: it is widely used for many classification problems, particularly ones with many features. However, L_1 regularization typically selects too many features and that so-called false positives are unavoidable. In this paper, we demonstrate and analyze an aggregation method for sparse logistic regression in high dimensions. This approach linearly combines the estimators from a suitable set of logistic
arXiv:1410.6959v2
fatcat:sh3qu3od6rdk7p5dtehbyrqtdm