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Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3748415/ https://www.ncbi.nlm.nih.gov/pubmed/23983813 http://dx.doi.org/10.1155/2013/768404 |
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author | Liu, Jianwei Li, Shuang Cheng Luo, Xionglin |
author_facet | Liu, Jianwei Li, Shuang Cheng Luo, Xionglin |
author_sort | Liu, Jianwei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3748415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37484152013-08-27 Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification Liu, Jianwei Li, Shuang Cheng Luo, Xionglin Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2013 2013-08-05 /pmc/articles/PMC3748415/ /pubmed/23983813 http://dx.doi.org/10.1155/2013/768404 Text en Copyright © 2013 Jianwei Liu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Jianwei Li, Shuang Cheng Luo, Xionglin Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification |
title | Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification |
title_full | Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification |
title_fullStr | Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification |
title_full_unstemmed | Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification |
title_short | Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification |
title_sort | iterative reweighted noninteger norm regularizing svm for gene expression data classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3748415/ https://www.ncbi.nlm.nih.gov/pubmed/23983813 http://dx.doi.org/10.1155/2013/768404 |
work_keys_str_mv | AT liujianwei iterativereweightednonintegernormregularizingsvmforgeneexpressiondataclassification AT lishuangcheng iterativereweightednonintegernormregularizingsvmforgeneexpressiondataclassification AT luoxionglin iterativereweightednonintegernormregularizingsvmforgeneexpressiondataclassification |