Choosing instance selection method using meta-learning

被引：0

作者：

Moura, Shayane de Oliveira ^{[1
,2
]}

de Freitas, Marcelo Bassani ^{[1
]}

Cardoso, Halisson A. C. ^{[1
]}

Cavalcanti, George D. C. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil

[2] Inst Fed Sertao Pernambucano, Dept Informat, Ouricuri, Brazil

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC) | 2014年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many instance selection methods (ISMs) have been widely studied and proposed. But none of these methods obtain good performance on every data set. In this work, we propose an architecture to select the best ISM for a given data set. We use meta-learning to train a meta-classifier that learns the relationship between the ISMs performance and the data set structure. The proposed method was evaluated on public data sets and showed better results than traditional approaches.

引用

页码：2003 / 2007

页数：5

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