Open AccessSmooth augmented Lagrangian method for twin bounded support vector machine
Author(s) -
Fatemeh Bazikar,
Saeed Ketabchi,
Hossein Moosaei
Publication year - 2022
Publication title -
numerical algebra, control and optimization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.303
H-Index - 20
eISSN - 2155-3289
pISSN - 2155-3297
DOI - 10.3934/naco.2021027
Subject(s) - augmented lagrangian method , bounded function , benchmark (surveying) , smoothing , minification , support vector machine , structural risk minimization , computer science , mathematical optimization , binary classification , mathematics , lagrangian , algorithm , artificial intelligence , mathematical analysis , geodesy , computer vision , geography
In this paper, we propose a method for solving the twin bounded support vector machine (TBSVM) for the binary classification. To do so, we use the augmented Lagrangian (AL) optimization method and smoothing technique, to obtain new unconstrained smooth minimization problems for TBSVM classifiers. At first, the augmented Lagrangian method is recruited to convert TBSVM into unconstrained minimization programming problems called as AL-TBSVM. We attempt to solve the primal programming problems of AL-TBSVM by converting them into smooth unconstrained minimization problems. Then, the smooth reformulations of AL-TBSVM, which we called AL-STBSVM, are solved by the well-known Newton's algorithm. Finally, experimental results on artificial and several University of California Irvine (UCI) benchmark data sets are provided along with the statistical analysis to show the superior performance of our method in terms of classification accuracy and learning speed.