A Simple and Effective Scheme for Data Pre-processing in Extreme Classification

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Abstract

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. It has been shown to be an effective framework for addressing crucial tasks such as recommendation, ranking and web-advertising. In this paper, we propose a method for effective and well-motivated data pre-processing scheme in XMC. We show that our proposed algorithm, PrunEX, can remove upto 90% data in the input which is redundant from a classification view-point. Our scheme is universal in the sense it is applicable to all known public datasets in the domain of XMC.

Details

Original languageEnglish
Title of host publicationESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publication statusPublished - 26 Apr 2019
MoE publication typeA4 Article in a conference publication
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 24 Apr 201926 Apr 2019

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN
CountryBelgium
CityBruges
Period24/04/201926/04/2019

ID: 36454472