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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.
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Contributor : Bikash Joshi Connect in order to contact the contributor
Submitted on : Tuesday, March 29, 2016 - 11:10:19 AM
Last modification on : Thursday, October 21, 2021 - 3:48:42 AM


  • HAL Id : hal-01294379, version 1


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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