Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.3233/978-1-60750-806-9-579 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
multi-class classification,binary classifiers,otoneurology,k-nearest neighbour method,support vector machines | Data mining,Nearest neighbour,Pattern recognition,Binary classification,Random subspace method,Support vector machine,Artificial intelligence,Classifier (linguistics),Medicine,Machine learning,Binary number | Conference |
Volume | ISSN | Citations |
169 | 0926-9630 | 6 |
PageRank | References | Authors |
0.62 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kirsi Varpa | 1 | 16 | 2.32 |
| Henry Joutsijoki | 2 | 46 | 8.41 |
| Kati Iltanen | 3 | 23 | 3.95 |
| Martti Juhola | 4 | 456 | 63.94 |