Name
Abstract | ||
|---|---|---|
Multi-label selective ensemble deals with the problem of reducing the size of multi-label ensembles whilst keeping or improving the performance. In practice, it is of important value, since the generated ensembles are usually unnecessarily large, which leads to extra high computational and storage cost. However, it is more challenging than traditional selective ensemble, because real-world applications often employ different performance measures to evaluate the quality of multi-label predictions, depending on user requirements. In this paper, we propose the MUSE approach to tackle this problem. Specifically, by directly considering the concerned performance measure, we develop a convex optimization formulation and provide an efficient stochastic optimization solution for a large variety of multi-label performance measures. Experiments show that MUSE is able to obtain smaller multi-label ensembles, whilst achieving better or at least comparable performance in terms of the concerned performance measure. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20248-8_7 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Multi-label classification,Ensemble pruning,Selective ensemble | Mathematical optimization,Stochastic optimization,Computer science,Multi-label classification,Convex optimization,User requirements document | Conference |
Volume | ISSN | Citations |
9132 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 27 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Li | 1 | 353 | 15.23 |
| Yuan Jiang | 2 | 714 | 53.61 |
| Zhi-Hua Zhou | 3 | 13480 | 569.92 |