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

Lan L, Vucetic S - BMC Proc (2013)

Bottom Line: A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples.To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied.The empirical results show that the proposed method achieved the most accurate predictions overall.

View Article: PubMed Central - HTML - PubMed

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 results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

No MeSH data available.


Average AUC score of different feature selection algorithms across different train sizes.
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Figure 1: Average AUC score of different feature selection algorithms across different train sizes.

Mentions: For each microarray data set, we randomly selected N+ = 2, 3, 4, 5 positive and the same number of negative examples as the training data and used the rest as the test data. We show the results for m = 100 in this section. The average AUC across these 8 microarray datasets is shown in Figure 1. The results clearly show the multi-task version of our proposed algorithm was the most successful algorithm overall.


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

Lan L, Vucetic S - BMC Proc (2013)

Average AUC score of different feature selection algorithms across different train sizes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4043987&req=5

Figure 1: Average AUC score of different feature selection algorithms across different train sizes.
Mentions: For each microarray data set, we randomly selected N+ = 2, 3, 4, 5 positive and the same number of negative examples as the training data and used the rest as the test data. We show the results for m = 100 in this section. The average AUC across these 8 microarray datasets is shown in Figure 1. The results clearly show the multi-task version of our proposed algorithm was the most successful algorithm overall.

Bottom Line: A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples.To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied.The empirical results show that the proposed method achieved the most accurate predictions overall.

View Article: PubMed Central - HTML - PubMed

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 results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

No MeSH data available.