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

Sujay Khandagale, Rohit Babbar

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

1 Sitaatiot (Scopus)
21 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Kustantajai6doc.com
Sivut67-72
ISBN (painettu)978-287-587-065-0
TilaJulkaistu - 26 huhtik. 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgia
Kesto: 24 huhtik. 201926 huhtik. 2019
Konferenssinumero: 27

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
LyhennettäESANN
Maa/AlueBelgia
KaupunkiBruges
Ajanjakso24/04/201926/04/2019

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