The Yahoo! Webscope™ Program is a reference library of interesting and scientifically useful datasets for non-commercial use by academics and other scientists. All datasets have been reviewed to conform to Yahoo!'s data protection standards, including strict controls on privacy. We have a number of datasets that we are excited to share with you. Learn how to get involved.
Fleiss' kappa is a statistical measure for assessing the reliability of agreement between a fixed number of raters when assigning categorical ratings to a number of items or classifying items. This contrasts with other kappas such as Cohen's kappa, which only work when assessing the agreement between two raters. The measure calculates the degree of agreement in classification over that which would be expected by chance and is scored as a number between 0 and 1. There is no generally agreed on measure of significance, although guidelines have been given.
A. Jaiswal, S. Singh, and S. Tripathy. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), page 1-6. IEEE, (July 2023)
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)
H. Zhang, A. Santos, and J. Freire. Proceedings of the 30th ACM International Conference on Information &$\mathsemicolon$ Knowledge Management, ACM, (October 2021)
K. Piczak. Proceedings of the 23rd ACM International Conference on Multimedia, page 1015–1018. New York, NY, USA, Association for Computing Machinery, (2015)
D. Schmidt, A. Zehe, J. Lorenzen, L. Sergel, S. Düker, M. Krug, and F. Puppe. Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 49--56. Punta Cana, Dominican Republic (online), Association for Computational Linguistics, (November 2021)
D. Schmidt, A. Zehe, J. Lorenzen, L. Sergel, S. Düker, M. Krug, and F. Puppe. Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 49--56. Punta Cana, Dominican Republic (online), Association for Computational Linguistics, (November 2021)
N. Dehouche, and A. Wongkitrungrueng. Proceedings of ANZMAC 2018: The 20th Conference of the Australian and New Zealand Marketing Academy. Adelaide (Australia), page 3--5 December. (2018)
J. Pfister, K. Kobs, and A. Hotho. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, page 816-825. (June 2021)
J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel. Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, page 43--52. (2015)
E. Žunić, K. Korjenić, S. Delalić, and Z. Šubara. International Journal of Computer Science and Information Technology (IJCSIT), 13 (2):
67 - 84(April 2021)