A survey on multi-output regression

被引：480

作者：

Borchani, Hanen ^{[1
]}

Varando, Gherardo ^{[2
]}

Bielza, Concha ^{[2
]}

Larranaga, Pedro ^{[2
]}

机构：

[1] Aalborg Univ, Dept Comp Sci, Machine Intelligence Grp, Aalborg, Denmark

[2] Univ Politecn Madrid, Dept Inteligencia Artificial, Fac Informat, Computat Intelligence Grp, Madrid, Spain

来源：

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY | 2015年 / 5卷 / 05期

关键词：

SUPPORT VECTOR REGRESSION; MODEL; INDUCTION; TREES;

D O I：

10.1002/widm.1157

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This study provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks. (C) 2015 John Wiley & Sons, Ltd.

引用

页码：216 / 233

页数：18

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