Name
Abstract | ||
|---|---|---|
The problem of imbalanced training sets in supervised pattern recognition methods is receiving growing attention. Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. It has been observed that this situation, which arises in several practical situations, may produce an important deterioration of the classification accuracy, in particular with patterns belonging to the less represented classes. In the present paper, we introduce a new approach to design an instance-based classifier in such imbalanced environments. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-44871-6_10 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
pattern recognition | Weighted distance,Weighting,Pattern recognition,Computer science,Supervised learning,Artificial intelligence,Classifier (linguistics),Resampling,Machine learning | Conference |
Volume | ISSN | Citations |
2652 | 0302-9743 | 4 |
PageRank | References | Authors |
0.60 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R Barandela | 1 | 558 | 23.46 |
| José Salvador Sánchez | 2 | 184 | 15.36 |
| Vicente García | 3 | 124 | 10.85 |
| Francesc J. Ferri | 4 | 356 | 38.92 |