Multi-task feature selection in microarray data by binary integer programming.

ABSTRACT: A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical res

SUBMITTER: Lan L 

PROVIDER: S-EPMC4043987 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature