基于偏最小二乘降维的分类模型比较

山东大学学报(工学版) ›› 2010, Vol. 40 ›› Issue (5): 41-47.

基于偏最小二乘降维的分类模型比较

曾雪强1,李国正2

1.南昌大学计算中心, 江西南昌 330009; 2. 同济大学控制科学与工程系, 上海 201804

收稿日期:2010-05-10 出版日期:2010-10-16 发布日期:2010-05-10
作者简介:曾雪强(1978-),男,博士,讲师,主要研究方向为数据挖掘.E-mail:xqzeng@ncu.edu.cn
基金资助:
国家自然科学基金资助项目(60873129)

An examination of classification model with partial least square based dimension reduction

ZENG Xue-qiang1, LI Guo-zheng2

1. Computer Center, Nanchang University, Nanchang 330009, China;
2. Department of Control Science and Engineering, Tongji University, Shanghai 201804, China

Received:2010-05-10 Online:2010-10-16 Published:2010-05-10

摘要/Abstract

摘要：

在众多数据降维方法中,偏最小二乘降维方法是一种非常有效的数据降维模型,并被广泛应用于生物基因数据分析等领域。但基于偏最小二乘降维的分类模型的选择问题,往往为以往的研究工作所忽视,研究者基本是根据自身喜好选择不同的分类模型。针对这一问题,本文通过大量的实验,对多种不同分类模型在生物基因芯片数据集上的性能进行了比较和分析。通过t检验,发现人工神经网络、逻辑斯特判别、线性支持向量机是3种在偏最小二乘降维上性能较好的的分类模型。

关键词: 数据降维, 偏最小二乘降维, 分类模型

Abstract:

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 to this problem, an examination of different classification model with PLSDR by intensive experiments was gived.Furthermore,by using paired twotailed ttest, artificial neural network, logistic discrimination and linear support vector machine were suggested to be well performance classification models used with PLSDR.

Key words: dimension reduction, partial least square based dimension reduction, classification model

曾雪强1,李国正2. 基于偏最小二乘降维的分类模型比较[J]. 山东大学学报(工学版), 2010, 40(5): 41-47.

ZENG Xue-qiang1, LI Guo-zheng2. An examination of classification model with partial least square based dimension reduction[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(5): 41-47.

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