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

Bikash Joshi 1 Massih-Reza Amini 1 Ioannis Partalas 2 Liva Ralaivola 3 Nicolas Usunier 4 Gaussier Eric 1
1 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble
2 VISEO
3 QARMA - éQuipe AppRentissage et MultimediA [Marseille]
LIF - Laboratoire d'informatique Fondamentale de Marseille
4 FAIR - Facebook AI Research [Paris]
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.
Keywords : Large scale multiclass classification reduction
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https://hal.archives-ouvertes.fr/hal-01294379
Contributor : Bikash Joshi <>
Submitted on : Tuesday, March 29, 2016 - 11:10:19 AM
Last modification on : Monday, April 20, 2020 - 11:24:01 AM

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  • HAL Id : hal-01294379, version 1

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PERSYVAL-LAB | UGA | CNRS | EC-MARSEILLE | UNIV-AMU | LIF | LIS-LAB | LIG

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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. ⟨hal-01294379⟩

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