Name
Abstract | ||
|---|---|---|
•New evolutionary ensemble model for multi-label classification.•Encodes the ensemble in the chromosome.•It considers the imbalance, dimensionality and relationships among the labels.•Comparison versus 12 algorithms over 14 datasets.•Achieved better and more consistent performance than state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.inffus.2018.11.013 | Information Fusion |
Keywords | Field | DocType |
Multi-label classification,Ensemble,Evolutionary algorithm | Control algorithm,Pattern recognition,Evolutionary algorithm,Artificial intelligence,Classifier (linguistics),Mathematics,Machine learning,Mutation operator | Journal |
Volume | ISSN | Citations |
50 | 1566-2535 | 2 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose M. Moyano | 1 | 52 | 3.41 |
| Eva Lucrecia Gibaja Galindo | 2 | 28 | 3.37 |
| Krzysztof J. Cios | 3 | 1121 | 100.59 |
| S. Ventura | 4 | 2318 | 158.44 |