On the fusion of threshold classifiers for categorization and dimensionality reduction

被引：0
|
作者：
Hans A. Kestler
Ludwig Lausser
Wolfgang Lindner
Günther Palm
机构：
[1] University Hospital Ulm,Internal Medicine I
[2] University of Ulm,Institute of Neural Information Processing
来源：
Computational Statistics | 2011年 / 26卷
关键词：
Feature reduction; Threshold classifiers; High dimensional data;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We study ensembles of simple threshold classifiers for the categorization of high-dimensional data of low cardinality and give a compression bound on their prediction risk. Two approaches are utilized to produce such classifiers. One is based on univariate feature selection employing the area under the ROC curve as ranking criterion. The other approach uses a greedy selection strategy. The methods are applied to artificial data, published microarray expression profiles, and highly imbalanced data.
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页码：321 / 340
页数：19
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