title: Applying one-vs-one and one-vs-all classifiers in k-nearest neighbour method and support vector machines to an otoneurological multi-class problem.
abstract: We studied how the splitting of a multi-class classification problem into multiple binary classification tasks, like One-vs-One (OVO) and One-vs-All (OVA), affects the predictive accuracy of disease classes. Classifiers were tested with an otoneurological data using 10-fold cross-validation 10 times with k-Nearest Neighbour (k-NN) method and Support Vector Machines (SVM). The results showed that the use of multiple binary classifiers improves the classification accuracies of disease classes compared to one multi-class classifier. In general, OVO classifiers worked out better with this data than OVA classifiers. Especially, the OVO with k-NN yielded the highest total classification accuracies.
authors: Computer Science, School of Information Sciences, University of Tampere, Finland. Kirsi.Varpa@cs.uta.fi
authors: Kirsi
authors: K
authors: Varpa
authors: Henry
authors: H
authors: Joutsijoki
authors: Kati
authors: K
authors: Iltanen
authors: Martti
authors: M
authors: Juhola
mesh_terms: D000465:Algorithms; D001331:Automation; D003937:Diagnosis, Differential; D004427:Ear Diseases / Q000145:classification / Q000175:diagnosis; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D013223:Statistics as Topic; D060388:Support Vector Machine