Name
Abstract | ||
|---|---|---|
Multilabel was introduced as an extension of multi-class classification to cope with complex learning tasks in different application fields as text categorization, video o music tagging or bio-medical labeling of gene functions or diseases. The aim is to predict a set of classes (called labels in this context) instead of a single one. In this paper we deal with the problem of feature selection in multilabel classification. We use a graphical model to represent the relationships among labels and features. The topology of the graph can be characterized in terms of relevance in the sense used in feature selection tasks. In this framework, we compare two strategies implemented with different multilabel learners. The strategy that considers simultaneously the set of all labels outperforms the method that considers each label separately. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24800-9_24 | IDA |
Keywords | Field | DocType |
feature selection,different multilabel learner,different application field,multilabel classification,multi-class classification,gene function,multilabel classification task,graphical feature selection,feature selection task,text categorization,complex learning task,graphical model | Graph,Feature selection,Pattern recognition,Computer science,Artificial intelligence,Graphical model,Text categorization,Machine learning | Conference |
Volume | ISSN | Citations |
7014 | 0302-9743 | 7 |
PageRank | References | Authors |
0.51 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gerardo Lastra | 1 | 10 | 0.89 |
| Oscar Luaces | 2 | 281 | 24.59 |
| José Ramón Quevedo | 3 | 175 | 15.37 |
| Antonio Bahamonde | 4 | 335 | 31.96 |