Least absolute deviation-based robust support vector regression

被引：22
|
作者：
Chen, Chuanfa [1 ,2 ,3 ]
Li, Yanyan [4 ]
Yan, Changqing [5 ]
Guo, Jinyun [1 ,2 ,3 ]
Liu, Guolin [3 ]
机构：
[1] Shandong Univ Sci & Technol, State Key Lab Min Disaster Prevent & Control Cofo, Qingdao 266590, Peoples R China
[2] Shandong Univ Sci & Technol, Minist Sci & Technol, Qingdao 266590, Peoples R China
[3] Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China
[4] Wuhan Univ, Shool Geodesy & Geomat, Wuhan 430072, Peoples R China
[5] Shandong Univ Sci & Technol, Dept Informat Engn, Tai An 271019, Shandong, Peoples R China
来源：
KNOWLEDGE-BASED SYSTEMS | 2017年 / 131卷
基金：
中国国家自然科学基金;
关键词：
Support vector regression; Robust; Outlier; Least absolute deviation; MACHINE; CLASSIFICATION; CLASSIFIERS; ALGORITHMS; OUTLIERS;
D O I：
10.1016/j.knosys.2017.06.009
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To suppress the influence of outliers on function estimation, we propose a least absolute deviation (LAD) based robust support vector regression (SVR). Furthermore, an efficient algorithm based on the split-Bregman iteration is introduced to solve the optimization problem of the proposed algorithm. Both artificial and benchmark datasets are employed to compare the performance of the proposed algorithm with those of least squares SVR (LS-SVR), and two weighted versions of LS-SVR with the weight functions of Hampel and Logistic, respectively. Experiments demonstrate the superiority of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：183 / 194
页数：12
相关论文
共 50 条
  • [31] Second order representations of the least absolute deviation regression estimator
    Arcones, MA
    [J]. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 1998, 50 (01) : 87 - 117
  • [32] Efficient Sparse Least Absolute Deviation Regression With Differential Privacy
    Liu, Weidong
    Mao, Xiaojun
    Zhang, Xiaofei
    Zhang, Xin
    [J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2024, 19 : 2328 - 2339
  • [33] Second Order Representations of the Least Absolute Deviation Regression Estimator
    Miguel A. Arcones
    [J]. Annals of the Institute of Statistical Mathematics, 1998, 50 : 87 - 117
  • [34] Robust Geographically Weighted Regression with Least Absolute Deviation Method in Case of Poverty in Java']Java Island
    Afifaha, Rawyanil
    Andriyana, Yudhie
    Jaya, I. G. N. Mindra
    [J]. STATISTICS AND ITS APPLICATIONS, 2017, 1827
  • [35] Robust Radial Basis Function Networks Based on Least Trimmed Squares-Support Vector Regression
    Su, Shun-Feng
    Jeng, Jin-Tsong
    Liu, Yue-Shiang
    Chuang, Chen-Chia
    Rudas, Imre J.
    [J]. PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS), 2013, : 1 - 6
  • [36] A novel robust regression approach of lidar signal based on modified least squares support vector machine
    Sun, BY
    Huang, DS
    Fang, HT
    Yang, XM
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2005, 19 (05) : 715 - 729
  • [37] A Framework Based on Support Vector Regression for Robust Optimization
    Yao, Biqiang
    [J]. MANUFACTURING ENGINEERING AND AUTOMATION I, PTS 1-3, 2011, 139-141 : 1073 - 1078
  • [38] A Robust Support Vector Regression Based on Fuzzy Clustering
    Shieh, Horng-Lin
    [J]. NEXT-GENERATION APPLIED INTELLIGENCE, PROCEEDINGS, 2009, 5579 : 262 - 270
  • [39] Instability of least squares, least absolute deviation and least median of squares linear regression - Comment
    Portnoy, S
    Mizera, I
    [J]. STATISTICAL SCIENCE, 1998, 13 (04) : 344 - 347
  • [40] Personalized Fitting with Deviation Adjustment Based on Support Vector Regression for Recommendation
    Li, Weimin
    Yao, Mengke
    Jin, Qun
    [J]. MULTIMEDIA AND UBIQUITOUS ENGINEERING, 2014, 308 : 173 - 178
12345