Name
Abstract | ||
|---|---|---|
The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along with the ratio between minority and majority classes. Experiments using eight different classifiers show that the most significant differences are for data sets with low or moderate imbalance: over-sampling clearly appears as better than under-sampling for local classifiers, whereas some under-sampling strategies outperform oversampling when employing classifiers with global learning. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13022-9_54 | IEA/AIE (1) |
Keywords | Field | DocType |
class imbalance problem,different classifier,under-sampling strategy,local classifier,distinct factor,present paper study,moderate imbalance,majority class,resampling strategy,global learning,imbalanced data set | Data set,Radial basis function,init,Pattern recognition,Oversampling,Computer science,Artificial intelligence,Classifier (linguistics),Resampling,Machine learning | Conference |
Volume | ISSN | ISBN |
6096 | 0302-9743 | 3-642-13021-6 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vicente García | 1 | 124 | 10.85 |
| José Salvador Sánchez | 2 | 184 | 15.36 |
| Ramón A. Mollineda | 3 | 383 | 20.41 |