Principal weighted support vector machines for sufficient dimension reduction in binary classification

被引：31

作者：

Shin, Seung Jun ^{[1
]}

Wu, Yichao ^{[2
]}

Zhang, Hao Helen ^{[3
]}

Liu, Yufeng ^{[4
]}

机构：

[1] Korea Univ, Dept Stat, Seoul 02841, South Korea

[2] North Carolina State Univ, Dept Stat, 2311 Stinson Dr,Campus Box 8203, Raleigh, NC 27695 USA

[3] Univ Arizona, Dept Math, 617 North Santa Rita Ave,POB 210089, Tucson, AZ 85721 USA

[4] Univ North Carolina Chapel Hill, Dept Stat & Operat Res, 354 Hanes Hall, Chapel Hill, NC 27599 USA

来源：

BIOMETRIKA | 2017年 / 104卷 / 01期

基金：

新加坡国家研究基金会; 美国国家卫生研究院; 美国国家科学基金会;

关键词：

Fisher consistency; Hyperplane alignment; Reproducing kernel Hilbert space; Weighted support vector machine; SLICED INVERSE REGRESSION; CENTRAL SUBSPACE; HESSIAN DIRECTIONS;

D O I：

10.1093/biomet/asw057

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are studied, and an efficient computing algorithm is proposed. Numerical examples demonstrate its performance in binary classification.

引用

页码：67 / 81

页数：15

共 50 条

[31] Application of weighted support vector machines to business stakeholder classification on the web
Jia, YS
Fourth Wuhan International Conference on E-Business: The Internet Era & The Global Enterprise, Vols 1 and 2, 2005, : 1157 - 1163
[32] Aerial LiDAR data classification using weighted support vector machines
Wu, J. (wujun93161@hotmail.com), 1600, Editorial Board of Medical Journal of Wuhan University (38):
[33] Aerial Lidar Data Classification Using Weighted Support Vector Machines
Wu Jun
Guo Ning
Liu Rong
Liu Lijuan
Xu Gang
THIRD INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2011), 2011, 8009
[34] Weighted Least Squares Twin Support Vector Machines for Pattern Classification
Chen, Jing
Ji, Guangrong
2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 2, 2010, : 242 - 246
[35] FEATURE REDUCTION FOR COMPUTATIONALLY EFFICIENT DAMAGE STATE CLASSIFICATION USING BINARY TREE SUPPORT VECTOR MACHINES
Coelho, Clyde
Chattopadhyay, Aditi
SMASIS 2008: PROCEEDINGS OF THE ASME CONFERENCE ON SMART MATERIALS, ADAPTIVE STRUCTURES AND INTELLIGENT SYSTEMS - 2008, VOL 2, 2009, : 289 - 296
[36] Evolving data-adaptive support vector machines for binary classification
Dudzik, Wojciech
Nalepa, Jakub
Kawulok, Michal
KNOWLEDGE-BASED SYSTEMS, 2021, 227
[37] Comparative efficiency of algorithms based on support vector machines for binary classification
Kadyrova N.O.
Pavlova L.V.
Biophysics, 2015, 60 (1) : 13 - 24
[38] Density-weighted support vector machines for binary class imbalance learning
Hazarika, Barenya Bikash
Gupta, Deepak
NEURAL COMPUTING & APPLICATIONS, 2021, 33 (09): : 4243 - 4261
[39] Density Weighted Twin Support Vector Machines for Binary Class Imbalance Learning
Hazarika, Barenya Bikash
Gupta, Deepak
NEURAL PROCESSING LETTERS, 2022, 54 (02) : 1091 - 1130
[40] Density Weighted Twin Support Vector Machines for Binary Class Imbalance Learning
Barenya Bikash Hazarika
Deepak Gupta
Neural Processing Letters, 2022, 54 : 1091 - 1130

← 1 2 3 4 5 →