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

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Conference article in proceedings
Date
2019-04-26
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Language
en
Pages
67-72
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ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
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.
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Khandagale , S & Babbar , R 2019 , A Simple and Effective Scheme for Data Pre-processing in Extreme Classification . in ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning . i6doc.com , pp. 67-72 , European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , Bruges , Belgium , 24/04/2019 .