Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

Joint Authors

Luo, Xionglin
Liu, Jianwei
Li, Shuang Cheng

Source

Computational and Mathematical Methods in Medicine

Issue

Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2013-08-05

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Medicine

Abstract EN

Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data.

L2 regularization has been commonly used.

If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance.

However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning.

We have therefore proposed an adaptive learning algorithm using the iterative reweighted p-norm regularization support vector machine for 0 < p ≤ 2.

A simulated data set was created to evaluate the algorithm.

It was shown that a p value of 0.8 was able to produce better feature selection rate with high accuracy.

Four cancer data sets from public data banks were used also for the evaluation.

All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using a p value less than L1 norm.

Moreover, we observe that the proposed Lp penalty is more robust to noise variables than the L1 and L2 penalties.

American Psychological Association (APA)

Liu, Jianwei& Li, Shuang Cheng& Luo, Xionglin. 2013. Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-497240

Modern Language Association (MLA)

Liu, Jianwei…[et al.]. Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-497240

American Medical Association (AMA)

Liu, Jianwei& Li, Shuang Cheng& Luo, Xionglin. Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-497240

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-497240