Least squares twin bounded support vector machines based on L1-norm distance metric for classification.
In: Pattern Recognition, Jg. 74 (2018-02-01), S. 434-447
academicJournal
Zugriff:
In this paper, we construct a least squares version of the recently proposed twin bounded support vector machine (TBSVM) for binary classification. As a valid classification tool, TBSVM attempts to seek two non-parallel planes that can be produced by solving a pair of quadratic programming problems (QPPs), but this is time-consuming. Here, we solve two systems of linear equations rather than two QPPs to avoid this deficiency. Furthermore, the distance in least squares TBSVM (LSTBSVM) is measured by L2-norm, but L1-norm distance is usually regarded as an alternative to L2-norm to improve model robustness in the presence of outliers. Inspired by the advantages of least squares twin support vector machine (LSTWSVM), TBSVM and L1-norm distance, we propose a LSTBSVM based on L1-norm distance metric for binary classification, termed as L1-LSTBSVM, which is specially designed for suppressing the negative effect of outliers and improving computational efficiency in large datasets. Then, we design a powerful iterative algorithm to solve the L1-norm optimal problems, and it is easy to implement and its convergence to an optimum solution is theoretically ensured. Finally, the feasibility and effectiveness of L1-LSTBSVM are validated by extensive experimental results on both UCI datasets and artificial datasets. [ABSTRACT FROM AUTHOR]
Titel: |
Least squares twin bounded support vector machines based on L1-norm distance metric for classification.
|
---|---|
Autor/in / Beteiligte Person: | Yan, He ; Ye, Qiaolin ; Zhang, Tian’an ; Yu, Dong-Jun ; Yuan, Xia ; Xu, Yiqing ; Fu, Liyong |
Link: | |
Zeitschrift: | Pattern Recognition, Jg. 74 (2018-02-01), S. 434-447 |
Veröffentlichung: | 2018 |
Medientyp: | academicJournal |
ISSN: | 0031-3203 (print) |
DOI: | 10.1016/j.patcog.2017.09.035 |
Schlagwort: |
|
Sonstiges: |
|