A cost-sensitive rotation forest algorithm for gene expression data classification

被引：53

作者：

Lu, Huijuan ^{[1
]}

Yang, Lei ^{[1
]}

Yan, Ke ^{[1
]}

Xue, Yu ^{[2
]}

Gao, Zhigang ^{[3
]}

机构：

[1] China Jiliang Univ, Coll Informat Engn, 258 Xueyuan St, Hangzhou 310018, Zhejiang, Peoples R China

[2] Nanjing Univ Informat Sci & Technol, Nanjing 210044, Jiangsu, Peoples R China

[3] Hangzhou Dianzi Univ, Coll Comp Sci, Hangzhou 310018, Zhejiang, Peoples R China

来源：

NEUROCOMPUTING | 2017年 / 228卷

基金：

中国国家自然科学基金;

关键词：

Gene expression data; Rotation forest; Cost-sensitive; Misclassification cost; Rejection cost; Test cost; EXTREME LEARNING-MACHINE; ENSEMBLE; FRAMEWORK; SELECTION; REGRESSION; DIAGNOSIS; MODEL;

D O I：

10.1016/j.neucom.2016.09.077

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Existing works show that the rotation forest algorithm has competitive performance in terms of classification accuracy for gene expression data. However, most existing works only focus on the classification accuracy and neglect the classification costs. In this study, we propose a cost-sensitive rotation forest algorithm for gene expression data classification. Three classification costs, namely misclassification cost, test cost and rejection cost, are embedded into the rotation forest algorithm. This extension of the rotation forest algorithm is named as cost-sensitive rotation forest algorithm. Experimental results show that the cost-sensitive rotation forest algorithms effectively reduce the classification cost and make the classification result more reliable.

引用

页码：270 / 276

页数：7

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