PCA-based feature transformation for classification: Issues in medical diagnostics

被引：47

作者：

Pechenizkiy, M ^{[1
]}

Tsymbal, A ^{[1
]}

Puuronen, S ^{[1
]}

机构：

[1] Univ Jyvaskyla, Dept Comp Sci & Informat Syst, SF-40351 Jyvaskyla, Finland

来源：

17TH IEEE SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS | 2004年

关键词：

D O I：

10.1109/CBMS.2004.1311770

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The goal of this paper is to propose, evaluate, and compare several data mining strategies that apply feature transformation for subsequent classification, and to consider their application to medical diagnostics. We (1) briefly consider the necessity of dimensionality reduction and discuss why feature transformation may work better than feature selection for some problems; (2) analyze experimentally whether extraction of new components and replacement of original features by them is better than storing the original features as well; (3) consider how important the use of class information is in the feature extraction process; and (4) discuss some interpretability issues regarding the extracted features.

引用

页码：535 / 540

页数：6