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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 prediction accuracy on four multiclass classification datasets by varying the number of samples from per subclass.
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f3: Comparison of prediction accuracy on four multiclass classification datasets by varying the number of samples from per subclass.

Mentions: The average prediction accuracy of the classification is shown in Fig. 3. The MRSRC achieves the best classification accuracy in all cases in the SRBCT data and LukemiaGloub data (Fig. 3a and d). In the ALL experiments (Fig. 3b), the proposed MRSRC shows no evident advantages over SRC and MSRC. The MRSRC achieves extremely high prediction accuracy in most cases for the MLLLeukemia data set (Fig. 3c). As shown by the data dimensions in the four experiments, the MRSCR offers more advantages and more stability than other methods.


Maxdenominator Reweighted Sparse Representation for Tumor Classification
Comparison of prediction accuracy on four multiclass classification datasets by varying the number of samples from per subclass.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Comparison of prediction accuracy on four multiclass classification datasets by varying the number of samples from per subclass.
Mentions: The average prediction accuracy of the classification is shown in Fig. 3. The MRSRC achieves the best classification accuracy in all cases in the SRBCT data and LukemiaGloub data (Fig. 3a and d). In the ALL experiments (Fig. 3b), the proposed MRSRC shows no evident advantages over SRC and MSRC. The MRSRC achieves extremely high prediction accuracy in most cases for the MLLLeukemia data set (Fig. 3c). As shown by the data dimensions in the four experiments, the MRSCR offers more advantages and more stability than other methods.

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.