| Téléchargement | - Voir le manuscrit accepté : Iterative classification for multiple target attributes (PDF, 733 Kio)
|
|---|
| DOI | Trouver le DOI : https://doi.org/10.1007/s10844-012-0224-5 |
|---|
| Auteur | Rechercher : Guo, Hongyu1; Rechercher : Létourneau, Sylvain1 |
|---|
| Affiliation | - Conseil national de recherches du Canada. Technologies de l'information et des communications
|
|---|
| Format | Texte, Article |
|---|
| Sujet | multi-target learning; multitask learning; iterative classification; data mining |
|---|
| Résumé | Many real-world applications require the simultaneous prediction of multiple target attributes. The techniques currently available for these problems either employ a global model that simultaneously predicts all target attributes or rely on the aggregation of individual models, each predicting one target. This paper introduces a novel solution. Our approach employs an iterative classification strategy to exploit the relationships among multiple target attributes to achieve higher accuracy. The computation scheme is developed as a wrapper in which many standard single-target classification algorithms can be simply “plugged-in” to simultaneously predict multiple targets. An empirical evaluation using eight data sets shows that the proposed method outperforms 1) an approach that constructs independent classifiers for each target, 2) a multitask neural network method, and 3) ensembles of multi-objective decision trees in terms of simultaneously predicting all target attributes correctly. |
|---|
| Date de publication | 2013-04-01 |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Numéro NPARC | 21262546 |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | ff6b32a6-a0ca-45bf-ad71-ba54d7acd766 |
|---|
| Enregistrement créé | 2013-03-13 |
|---|
| Enregistrement modifié | 2020-06-04 |
|---|
