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Maxdenominator Reweighted Sparse Representation for Tumor Classification

View Article: PubMed Central - PubMed

ABSTRACT

The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods.

No MeSH data available.


Comparison of accuracy on four binary classification datasets by varying the number of top selected genes.
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f4: Comparison of accuracy on four binary classification datasets by varying the number of top selected genes.

Mentions: Figure 4 presents the result of the binary classification. The MRSRC outperforms the other methods in terms of prediction accuracy for all data sets, except for the DLBCL data set (Fig. 4d). The gene selection of the MRSRC is better than that of other methods for the acute leukemia, colon, and gliomas data sets (Fig. 4a,b and c). The MRSRC, SRC, and MSRC share the same curve trend. Evidently, the MRSRC, SRC, and MSRC consistently outperform LDASVM, ICASVM, MACE, and OTSDF in all data sets.


Maxdenominator Reweighted Sparse Representation for Tumor Classification
Comparison of accuracy on four binary classification datasets by varying the number of top selected genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5385541&req=5

f4: Comparison of accuracy on four binary classification datasets by varying the number of top selected genes.
Mentions: Figure 4 presents the result of the binary classification. The MRSRC outperforms the other methods in terms of prediction accuracy for all data sets, except for the DLBCL data set (Fig. 4d). The gene selection of the MRSRC is better than that of other methods for the acute leukemia, colon, and gliomas data sets (Fig. 4a,b and c). The MRSRC, SRC, and MSRC share the same curve trend. Evidently, the MRSRC, SRC, and MSRC consistently outperform LDASVM, ICASVM, MACE, and OTSDF in all data sets.

View Article: PubMed Central - PubMed

ABSTRACT

The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods.

No MeSH data available.