Name
Abstract | ||
|---|---|---|
We propose a novel cost-sensitive multi-label classification algorithm called cost-sensitive random pair encoding (CSRPE). CSRPE reduces the cost-sensitive multi-label classification problem to many cost-sensitive binary classification problems through the label powerset approach followed by the classic one-versus-one decomposition. While such a naive reduction results in exponentially-many classifiers, we resolve the training challenge of building the many classifiers by random sampling, and the prediction challenge of voting from the many classifiers by nearest-neighbor decoding through casting the one-versus-one decomposition as a special case of error-correcting code. Extensive experimental results demonstrate that CSRPE achieves stable convergence and reaches better performance than other ensemble-learning and error-correcting-coding algorithms for multi-label classification. The results also justify that CSRPE is competitive with state-of-the-art cost-sensitive multi-label classification algorithms for cost-sensitive multi-label classification. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Learning | Convergence (routing),One-class classification,Binary classification,Pattern recognition,Computer science,Multi-label classification,Artificial intelligence,Decoding methods,Linear classifier,Statistical classification,Machine learning,Encoding (memory) |
DocType | Volume | Citations |
Journal | abs/1611.09461 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao-Yuan Yang | 1 | 0 | 0.68 |
| Chih-Wei Chang | 2 | 9 | 1.90 |
| Hsuan-Tien Lin | 3 | 829 | 74.77 |