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
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
收藏
页码:216 / 233
页数:18
相关论文
共 74 条
[1]  
Abraham Zubin, 2013, Machine Learning and Knowledge Discovery in Databases. European Conference (ECML PKDD 2013). Proceedings: LNCS 8189, P320, DOI 10.1007/978-3-642-40991-2_21
[2]   Rule Ensembles for Multi-Target Regression [J].
Aho, Timo ;
Zenko, Bernard ;
Dzeroski, Saso .
2009 9TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, 2009, :21-+
[3]  
Ali Jalali, 2010, Advances in Neural Information Processing Systems, P964
[4]   Kernels for Vector-Valued Functions: A Review [J].
Alvarez, Mauricio A. ;
Rosasco, Lorenzo ;
Lawrence, Neil D. .
FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2012, 4 (03) :195-266
[5]  
[Anonymous], 2007, INT J DATA WAREHOUSI
[6]  
[Anonymous], 2009, J MACH LEARN RES
[7]  
[Anonymous], PATTERN RECOGN
[8]  
[Anonymous], 1995, INT C NEURAL NET
[9]  
[Anonymous], MVPART MULTIVARIATE
[10]  
[Anonymous], P 8 INT C SIGN IM TE