Combining localized fusion and dynamic selection for high-performance SVM. Issue 1 (January 2015) - Min, Jun-Ki; Hong, Jin-Hyuk; Cho, Sung-Bae
Highlights We present multiple SVMs combined by localized fusion and dynamic selection. Clustering OVA SVMs trained for each class constructs multiple decision templates. A naïve Bayes classifier selects the localized models dynamically for a new sample. Experiments on nine benchmark datasets show its superiority against alternatives. It proves to effectively manage the unbiased-variance and bias in real datasets.