An ensemble of filters and classifiers for microarray data classification

被引：127

作者：

Bolon-Canedo, V. ^{[1
]}

Sanchez-Marono, N. ^{[1
]}

Alonso-Betanzos, A. ^{[1
]}

机构：

[1] Univ A Coruna, Dept Comp Sci, Lab Res & Dev Artificial Intelligence LIDIA, La Coruna 15071, Spain

来源：

PATTERN RECOGNITION | 2012年 / 45卷 / 01期

关键词：

Feature selection; Ensemble methods for classification; Microarray data sets; GENE SELECTION; CANCER; TUMOR; PREDICTION; PATTERNS;

D O I：

10.1016/j.patcog.2011.06.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a new framework for feature selection consisting of an ensemble of filters and classifiers is described. Five filters, based on different metrics, were employed. Each filter selects a different subset of features which is used to train and to test a specific classifier. The outputs of these five classifiers are combined by simple voting. In this study three well-known classifiers were employed for the classification task: C4.5, naive-Bayes and IB1. The rationale of the ensemble is to reduce the variability of the features selected by filters in different classification domains. Its adequacy was demonstrated by employing 10 microarray data sets. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：531 / 539

页数：9

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