Binary Transformation Method for Multi-Label Stream Classification-Reference-Cited by-同舟云学术

Binary Transformation Method for Multi-Label Stream Classification

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Gulcan Ege Berkay¹,Ecevit Isin Su¹,Can Fazli¹

Affiliation:

1. Bilkent University, Ankara, Turkey

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557553

Reference35 articles.

1. Learning multi-label scene classification

2. Alican Büyükcc akir, Hamed Bonab , and Fazli Can . 2018 . A novel online stacked ensemble for multi-label stream classification . In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1063--1072 . Alican Büyükcc akir, Hamed Bonab, and Fazli Can. 2018. A novel online stacked ensemble for multi-label stream classification. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1063--1072.

3. Multi-label Problem Transformation Methods: a Case Study

4. Deciding among Fake, Satirical, Objective and Legitimate news

5. Ege Berkay Gulcan and Fazli Can . 2022. Unsupervised concept drift detection for multi-label data streams. Artificial Intelligence Review ( 2022 ), 1--34. Ege Berkay Gulcan and Fazli Can. 2022. Unsupervised concept drift detection for multi-label data streams. Artificial Intelligence Review (2022), 1--34.

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