|Title||Utility-based Weighted Multicategory Robust Support Vector Machines.|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Liu, Yufeng, Yichao Wu, and Qinying He|
|Date Published||2010 Oct 01|
The Support Vector Machines (SVM) has been an important classification technique in both machine learning and statistics communities. The robust SVM is an improved version of the SVM so that the resulting classifier can be less sensitive to outliers. In many practical problems, it may be advantageous to use different weights for different types of misclassification. However, the existing RSVM treats different kinds of misclassification equally. In this paper, we propose the weighted RSVM, as an extension of the standard SVM. We show that surprisingly, the cost-based weights do not work well for weighted extensions of the RSVM. To solve this problem, we propose a novel utility-based weights for the weighted RSVM. Both theoretical and numerical studies are presented to investigate the performance of the proposed weighted multicategory RSVM.
|Short Title||Utility-based weighted multicategory robust support vector machines.|
|Alternate Journal||Stat Interface|
|Original Publication||Utility-based weighted multicategory robust support vector machines.|
|PubMed Central ID||PMC3722909|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA149569 / CA / NCI NIH HHS / United States
R01 CA149569-01 / CA / NCI NIH HHS / United States
Utility-based Weighted Multicategory Robust Support Vector Machines.