Name
Abstract | ||
|---|---|---|
Existing learning models for classification of imbalanced data sets can be grouped as either boundary-based or nonboundary-based depending on whether a decision hyperplane is used in the learning process. The focus of this paper is a new approach that leverages the advantage of both approaches. Specifically, our new model partitions the input space into three parts by creating two additional bound... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2676239 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Training,Learning systems,Testing,Adaptation models,Support vector machines,Degradation,Predictive models | Heuristic,Data set,Pattern recognition,Computer science,Support vector machine,Moore–Penrose pseudoinverse,Learning models,Artificial intelligence,Linear discriminant analysis,Hyperplane,Classifier (linguistics),Machine learning | Journal |
Volume | Issue | ISSN |
29 | 6 | 2162-237X |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yujin Zhu | 1 | 36 | 5.28 |
| Zhe Wang | 2 | 268 | 18.89 |
| Hongyuan Zha | 3 | 6703 | 422.09 |
| Daqi Gao | 4 | 110 | 16.30 |