On the fusion of threshold classifiers for categorization and dimensionality reduction

被引：18

作者：

Kestler, Hans A. ^{[1
,2
]}

Lausser, Ludwig ^{[2
]}

Lindner, Wolfgang ^{[2
]}

Palm, Guenther ^{[1
]}

机构：

[1] Univ Ulm, Inst Neural Informat Proc, D-89069 Ulm, Germany

[2] Univ Hosp Ulm, D-89081 Ulm, Germany

来源：

COMPUTATIONAL STATISTICS | 2011年 / 26卷 / 02期

关键词：

Feature reduction; Threshold classifiers; High dimensional data; MOLECULAR CLASSIFICATION; LEARNING ALGORITHMS; PREDICTION; CANCER; COMPRESSION; TUMOR;

D O I：

10.1007/s00180-011-0243-7

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：321 / 340

页数：20

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