Effect of Distance Metrics on Multi-label Classification-Reference-Cited by-同舟云学术

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Effect of Distance Metrics on Multi-label Classification

Published:2021 Issue: Volume: Page:73-82
ISSN:2367-3370
Container-title:Proceeding of First Doctoral Symposium on Natural Computing Research
language:
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Author:

Tidake Vaishali S.,Sane Shirish S.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-33-4073-2_8

Reference14 articles.

1. Dai Y, Li Y, Li S-T (2020) Multi-label learning for concept-oriented labels of product image data. Image Vis Comput 93:103821

2. Madjarov G, Kocev D, Gjorgjevikj D, Džeroski S (2012) An extensive experimental comparison of methods for multi-label learning. Pattern Recognit 45(9):3084–3104

3. Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26:1819–1837. https://doi.org/10.1109/TKDE.2013.39

4. Tidake VS, Sane SS (2018) Multi-label classification: a survey. Int J Eng Technol 7(4.19):1045–1054

5. Zhang ML, Zhou ZH (2007) ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effective Multi-Label Classification Using Data Preprocessing;Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance;2021

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