Combining random forest and support vector machines for semi-supervised learning
Christos K. Aridas, Sotiris B. Kotsiantis · 2015
Semi-supervised classification methods use available unlabeled data, along with a small set of labeled examples, to increase the classification accuracy in comparison with classic supervised methods. In this work, an ensemble co-training method that combines the power of Random Forest and Support Vector Machines is presented. We performed a comparison with other well-known semi-supervised classification methods on standard benchmark datasets and the presented technique had better accuracy in most cases.