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.
| Original language | English |
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| Title of host publication | ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Publisher | i6doc.com |
| Pages | 67-72 |
| ISBN (Print) | 978-287-587-065-0 |
| Publication status | Published - 26 Apr 2019 |
| MoE publication type | A4 Conference publication |
| Event | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium Duration: 24 Apr 2019 → 26 Apr 2019 Conference number: 27 |
Conference
| Conference | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
|---|---|
| Abbreviated title | ESANN |
| Country/Territory | Belgium |
| City | Bruges |
| Period | 24/04/2019 → 26/04/2019 |