On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

Erik Schultheis; Rohit Babbar; Marek Wydmuch; Krzysztof Dembczynski

doi:10.1145/3534678.3539466

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

Erik Schultheis, Rohit Babbar, Marek Wydmuch, Krzysztof Dembczynski

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

25 Sitaatiot (Scopus)

77 Lataukset (Pure)

Abstrakti

The propensity model introduced by Jain et al has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Kustantaja	ACM
Sivut	1547–1557
Sivumäärä	11
ISBN (elektroninen)	978-1-4503-9385-0
DOI - pysyväislinkit	https://doi.org/10.1145/3534678.3539466
Tila	Julkaistu - elok. 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Washington, Yhdysvallat Kesto: 14 elok. 2022 → 18 elok. 2022 Konferenssinumero: 28 https://kdd.org/kdd2022/

Conference

Conference	ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Lyhennettä	KDD
Maa/Alue	Yhdysvallat
Kaupunki	Washington
Ajanjakso	14/08/2022 → 18/08/2022
www-osoite	https://kdd.org/kdd2022/

Pääsy asiakirjaan

10.1145/3534678.3539466

3534678.3539466Final published version, 1,55 MB

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

HPC-HD/Babbar: High Performance Computing for the Detection and Analysis of Historical Discourses
Babbar, R. (Vastuullinen tutkija)
01/01/2022 → 31/12/2024
Projekti: Academy of Finland: Other research funding

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
Schultheis, E. (Puhuja)
16 elok. 2022
Aktiviteetti: Konferenssiesitelmä

Siteeraa tätä

@inproceedings{28259d188a054073a16251902649b763,

title = "On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification",

abstract = "The propensity model introduced by Jain et al has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.",

author = "Erik Schultheis and Rohit Babbar and Marek Wydmuch and Krzysztof Dembczynski",

year = "2022",

month = aug,

doi = "10.1145/3534678.3539466",

language = "English",

pages = "1547–1557",

booktitle = "Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",

publisher = "ACM",

address = "United States",

note = "ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ; Conference date: 14-08-2022 Through 18-08-2022",

url = "https://kdd.org/kdd2022/",

}

Schultheis, E, Babbar, R, Wydmuch, M & Dembczynski, K 2022, On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. julkaisussa Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, Sivut 1547–1557, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, District of Columbia, Yhdysvallat, 14/08/2022. https://doi.org/10.1145/3534678.3539466

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. / Schultheis, Erik; Babbar, Rohit; Wydmuch, Marek et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022. s. 1547–1557.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

AU - Schultheis, Erik

AU - Babbar, Rohit

AU - Wydmuch, Marek

AU - Dembczynski, Krzysztof

N1 - Conference code: 28

PY - 2022/8

Y1 - 2022/8

N2 - The propensity model introduced by Jain et al has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.

AB - The propensity model introduced by Jain et al has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.

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U2 - 10.1145/3534678.3539466

DO - 10.1145/3534678.3539466

M3 - Conference article in proceedings

SP - 1547

EP - 1557

BT - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

PB - ACM

T2 - ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Y2 - 14 August 2022 through 18 August 2022

ER -

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

Abstrakti

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Projektit

HPC-HD/Babbar: High Performance Computing for the Detection and Analysis of Historical Discourses

Aktiviteetit

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

Siteeraa tätä