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題名 Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM 作者 周珮婷
Chou, Elizabeth P.
Ko, Tzu-Wei貢獻者 統計系 日期 2017-11 上傳時間 3-Jul-2018 15:05:09 (UTC+8) 摘要 The current study proposes a dimension reduction method, stepwise support vector machine (SVM), to reduce the dimensions of large p small n datasets. The proposed method is compared with other dimension reduction methods, namely, the Pearson product difference correlation coefficient (PCCs), recursive feature elimination based on random forest (RF-RFE), and principal component analysis (PCA), by using five gene expression datasets. Additionally, the prediction performance of the variables selected by our method is evaluated. The study found that stepwise SVM can effectively select the important variables and achieve good prediction performance. Moreover, the predictions of stepwise SVM for reduced datasets was better than those for the unreduced datasets. The performance of stepwise SVM was more stable than that of PCA and RF-RFE, but the performance difference with respect to PCCs was minimal. It is necessary to reduce the dimensions of large p small n datasets. We believe that stepwise SVM can effectively eliminate noise in data and improve the prediction accuracy in any large p small n dataset. 關聯 arXiv:1711.03346v1 資料類型 article dc.contributor 統計系 dc.creator (作者) 周珮婷 zh_TW dc.creator (作者) Chou, Elizabeth P. en_US dc.creator (作者) Ko, Tzu-Wei en_US dc.date (日期) 2017-11 dc.date.accessioned 3-Jul-2018 15:05:09 (UTC+8) - dc.date.available 3-Jul-2018 15:05:09 (UTC+8) - dc.date.issued (上傳時間) 3-Jul-2018 15:05:09 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118191 - dc.description.abstract (摘要) The current study proposes a dimension reduction method, stepwise support vector machine (SVM), to reduce the dimensions of large p small n datasets. The proposed method is compared with other dimension reduction methods, namely, the Pearson product difference correlation coefficient (PCCs), recursive feature elimination based on random forest (RF-RFE), and principal component analysis (PCA), by using five gene expression datasets. Additionally, the prediction performance of the variables selected by our method is evaluated. The study found that stepwise SVM can effectively select the important variables and achieve good prediction performance. Moreover, the predictions of stepwise SVM for reduced datasets was better than those for the unreduced datasets. The performance of stepwise SVM was more stable than that of PCA and RF-RFE, but the performance difference with respect to PCCs was minimal. It is necessary to reduce the dimensions of large p small n datasets. We believe that stepwise SVM can effectively eliminate noise in data and improve the prediction accuracy in any large p small n dataset. en_US dc.format.extent 98 bytes - dc.format.mimetype text/html - dc.relation (關聯) arXiv:1711.03346v1 dc.title (題名) Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM en_US dc.type (資料類型) article
