A Cross-Conformal Predictor for Multi-label Classification

Abstract : Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently developed framework called Conformal Prediction (CP) to the multi-label learning setting. CP complements the predictions of machine learning algorithms with reliable measures of confidence. As a result the proposed approach instead of just predicting the most likely subset of classes for a new unseen instance, also indicates the likelihood of each predicted subset being correct. This additional information is especially valuable in the multi-label setting where the overall uncertainty is extremely high.
Type de document :
Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.241-250, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_26〉
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01391051
Contributeur : Hal Ifip <>
Soumis le : mercredi 2 novembre 2016 - 17:17:54
Dernière modification le : vendredi 1 décembre 2017 - 01:16:37
Document(s) archivé(s) le : vendredi 3 février 2017 - 15:51:19

Fichier

978-3-662-44722-2_26_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Harris Papadopoulos. A Cross-Conformal Predictor for Multi-label Classification. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.241-250, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_26〉. 〈hal-01391051〉

Partager

Métriques

Consultations de la notice

53

Téléchargements de fichiers

25