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Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations
Feature selection can remove non-important features from the data and promote better classifiers. This task, when applied to multi-label data where each instance is associated with a set of labels, supports emerging applications. Although multi-label data usually exhibit label relations, label depen...
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| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer
2015-12-01
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| Colecção: | International Journal of Computational Intelligence Systems |
| Assuntos: | |
| Acesso em linha: | https://www.atlantis-press.com/article/25868669.pdf |
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