Name
Abstract | ||
|---|---|---|
A widely used approach to cope with asymmetry in dissimilarities is by symmetrizing them. Usually, asymmetry is corrected by applying combiners such as average, minimum or maximum of the two directed dissimilarities. Whether or not these are the best approaches for combining the asymmetry remains an open issue. In this paper we study the performance of the extended asymmetric dissimilarity space (EADS) as an alternative to represent asymmetric dissimilarities for classification purposes. We show that EADS outperforms the representations found from the two directed dissimilarities as well as those created by the combiners under consideration in several cases. This holds specially for small numbers of prototypes; however, for large numbers of prototypes the EADS may suffer more from overfitting than the other approaches. Prototype selection is recommended to overcome overfitting in these cases. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39140-8_5 | SIMBAD |
Keywords | Field | DocType |
best approach,asymmetric dissimilarity,large number,extended asymmetric dissimilarity space,prototype selection,classification purpose,small number,open issue | Artificial intelligence,Overfitting,Asymmetry,Machine learning,Mathematics | Conference |
Citations | PageRank | References |
6 | 0.49 | 19 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yenisel Plasencia Calana | 1 | 44 | 5.41 |
| Veronika Cheplygina | 2 | 171 | 15.31 |
| Robert P. W. Duin | 3 | 4322 | 336.00 |
| Edel Garcia-Reyes | 4 | 95 | 12.84 |
| Mauricio Orozco-Alzate | 5 | 79 | 17.27 |
| David M. J. Tax | 6 | 2071 | 148.87 |
| Marco Loog | 7 | 1796 | 154.31 |