2012 | OriginalPaper | Chapter
The Comparison of Classification Model with Partial Least Square Based Dimension Reduction
Author : Su-Fen Chen
Published in: Advances in Computer, Communication, Control and Automation
Publisher: Springer Berlin Heidelberg
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Dimension reduction is a very important technique to handle with the analysis of high dimensional data sets. Among various methods, Partial Least Square based Dimension Reduction (PLSDR) is one of the most effective one, which has been applied in many fields such as the analysis of microarray data. But the problem of choosing classification model with PLSDR has often been neglected, different classification models are applied arbitrary. Aim at this problem, the paper gives an examination of different classification model with PLSDR by intensive experiments. Furthermore, some interesting conclusions are presented.