Enhancing accuracy of multi-label classification by applying one-vs-one support vector machine
Suthipong Daengduang, Peerapon Vateekul · 2016
Multi-label classification is a supervised learning, where one example can belong to several classes. In the case of Support Vector Machine (SVM), One-versus-All (OVA) is the most common approach to tackle this problem. However, the accuracy is very limited due to extremely imbalanced training set. It is interesting that there have only very few works that applied One-versus-One (OVO) in the multi-label domain even though it has been shown to provide better accuracy than OVA in the multiclass domain. In this paper, we propose a multi-label classification framework that employs OVO incorporating with the undersampling technique to alleviate the imbalanced issue. In the experiment, there are five standard benchmarks. The results show that our proposed algorithm outperforms OVA and traditional OVO in all data sets in terms of accuracy and F1.