Explaining the performance of multilabel classification methods with data set properties-Reference-Cited by-同舟云学术

Explaining the performance of multilabel classification methods with data set properties

Published:2022-02-04 Issue:9 Volume:37 Page:6080-6122
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J of Intelligent Sys

Author:

Bogatinovski Jasmin¹²³,Todorovski Ljupčo¹⁴,Džeroski Sašo¹²,Kocev Dragi¹²⁵

Affiliation:

1. Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

2. Jožef Stefan IPSchool Ljubljana Slovenia

3. Department of Distributed Operating Systems TU Berlin Germany

4. Department of Mathematics, Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia

5. Bias Variance Labs Ljubljana Slovenia

Publisher

Hindawi Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22835

Reference67 articles.

1. KrizhevskyA SutskeverI HintonG. ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe NV;2012;1:1097‐1105.

2. ElisseeffA WestonJ. A kernel method for multi‐labelled classification. In: Proceedings of the 14th International Conference on Neural Information Processing Systems. Vancouver;2001;14:681‐687.

3. Combined chemical genetics and data‐driven bioinformatics approach identifies receptor tyrosine kinase inhibitors as host‐directed antimicrobials;Cornelis K;Nat Commun,2018

4. No free lunch theorems for optimization

5. ThorntonC HutterF HoosHH Leyton‐BrownK. Auto‐WEKA: Combined selection and hyperparameter optimization of classification algorithms. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago IL;2013.doi:10.1145/2487575.2487629

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