Name
Abstract | ||
|---|---|---|
In multilabel classification tasks the aim is to find hypotheses able to predict, for each instance, a set of classes or labels rather than a single one. Some state-of-the-art multilabel learners use a thresholding strategy, which consists in computing a score for each label and then predicting the set of labels whose score is higher than a given threshold. When this score is the estimated posterior probability, the selected threshold is typically 0.5. In this paper we introduce a family of thresholding strategies which take into account the posterior probability of all possible labels to determine a different threshold for each instance. Thus, we exploit some kind of interdependence among labels to compute this threshold, which is optimal regarding a given expected loss function. We found experimentally that these strategies outperform other thresholding options for multilabel classification. They provide an efficient method to implement a learner which considers the interdependence among labels in the sense that the overall performance of the prediction of a set of labels prevails over that of each single label. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patcog.2011.08.007 | Pattern Recognition |
Keywords | Field | DocType |
probabilistic thresholding strategy,state-of-the-art multilabel learner,thresholding strategy,multilabel classification,selected threshold,possible label,multilabel classification task,multilabel classifier,posterior probability,thresholding option,estimated posterior probability,different threshold | Expected loss,Pattern recognition,Exploit,Posterior probability,Artificial intelligence,Thresholding,Probabilistic logic,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
45 | 2 | 0031-3203 |
Citations | PageRank | References |
32 | 1.07 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Ramón Quevedo | 1 | 175 | 15.37 |
| Oscar Luaces | 2 | 281 | 24.59 |
| Antonio Bahamonde | 3 | 335 | 31.96 |