Multi-class to Binary reduction of Large-scale classification Problems

Abstract : Large-scale multi-class classification problems have gained increased popularity in recent time mainly because of the overwhelming growth of textual and visual data in the web. However, this is a challenging task for many reasons. The main challenges in Large-scale classification problems are: scalability, complexity of model and class imbalance problem. In this work, we present an algorithm for binary reduction of multi-class classification problems, which aims at addressing the above-mentioned challenges.
Type de document :
Communication dans un congrès
International Workshop on Big Multi-Target Prediction ECML/PKDD 2015, Sep 2015, Poto, Portugal. 〈http://www.kermit.ugent.be/big-multi-target-prediction/papers.php〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01294379
Contributeur : Bikash Joshi <>
Soumis le : mardi 29 mars 2016 - 11:10:19
Dernière modification le : jeudi 11 octobre 2018 - 08:48:04

Identifiants

  • HAL Id : hal-01294379, version 1

Citation

Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Liva Ralaivola, Nicolas Usunier, et al.. Multi-class to Binary reduction of Large-scale classification Problems. International Workshop on Big Multi-Target Prediction ECML/PKDD 2015, Sep 2015, Poto, Portugal. 〈http://www.kermit.ugent.be/big-multi-target-prediction/papers.php〉. 〈hal-01294379〉

Partager

Métriques

Consultations de la notice

171