Name
Abstract | ||
|---|---|---|
In classification problems, a dataset is said to be imbalanced when the distribution of the target variable is very unequal. Classes contribute unequally to the decision boundary, and special metrics are used to evaluate these datasets. In previous work, we presented pairwise ranking as a method for binary imbalanced classification, and extended to the ordinal case using weights. In this work, we extend ordinal classification using traditional balancing methods. A comparison is made against traditional and ordinal SVMs, in which the ranking adaption proposed is found to be competitive. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59147-6_46 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II |
Keywords | Field | DocType |
Ordinal classification,Class imbalance,Ranking,SVM | Pairwise comparison,Pattern recognition,Ranking,Ordinal number,Computer science,Support vector machine,Artificial intelligence,Decision boundary,Ordinal optimization,Machine learning,Binary number | Conference |
Volume | ISSN | Citations |
10306 | 0302-9743 | 3 |
PageRank | References | Authors |
0.43 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Cruz | 1 | 10 | 3.28 |
| Kelwin Fernandes | 2 | 36 | 7.71 |
| Joaquim F. Pinto da Costa | 3 | 138 | 8.08 |
| María Pérez-Ortiz | 4 | 61 | 12.51 |
| Jaime S. Cardoso | 5 | 543 | 68.74 |