GenSVM: A Generalized Multiclass Support Vector Machine

Gertjan Burg, Patrick Groenen

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)

Abstract

Traditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem. Here, a generalized multiclass SVM is proposed called GenSVM. In this method classication boundaries for a K-class problem are constructed in a (K ? 1)-dimensional space using a simplex encoding. Additionally, several dierent weightings of the misclassication errors are incorporated in the loss function, such that it generalizes three existing multiclass SVMs through a single optimization problem. An iterative majorization algorithm is derived that solves the optimization problem without the need of a dual formulation. This algorithm has the advantage that it can use warm starts during cross validation and during a grid search, which signicantly speeds up the training phase. Rigorous numerical experiments compare linear GenSVM with seven existing multiclass SVMs on both small and large data sets. These comparisons show that the proposed method is competitive with existing methods in both predictive accuracy and training time, and that it signicantly outperforms several existing methods on these criteria.
Original languageEnglish
Pages (from-to)1-42
Number of pages42
JournalJournal of Machine Learning Research
Volume17
Issue number225
Publication statusPublished - 2016

Fingerprint

Dive into the research topics of 'GenSVM: A Generalized Multiclass Support Vector Machine'. Together they form a unique fingerprint.

Cite this