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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/118191

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118191

Title:	Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM
Authors:	周珮婷 Chou, Elizabeth P. Ko, Tzu-Wei
Contributors:	統計系
Date:	2017-11
Issue Date:	2018-07-03 15:05:09 (UTC+8)
Abstract:	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.
Relation:	arXiv:1711.03346v1
Data Type:	article
Appears in Collections:	[Department of Statistics] Periodical Articles

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