Research Publications for Bernhard M Pfahringer
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Pfahringer, B. (2020). Massage. In Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering (pp. xiii). IEEE. doi:10.1109/Confluence47617.2020.9058231
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PFAHRINGER, Bernhard MBahri, M., Pfahringer, B., Bifet, A., & Maniu, S. (2020). Efficient batch-incremental classification using UMAP for evolving data streams. In M. R. Berthold, A. Feelders, & G. Krempl (Eds.), Proc Advances in Intelligent Data Analysis IVIII: 18th Symposium on Intelligent Data Analysis (IDA 2020) Vol. LNCS 12080 (pp. 40-53). Konstanz, Germany: Springer. doi:10.1007/978-3-030-44584-3_4
Rivolli, A., Read, J., Soares, C., Pfahringer, B., & de Carvalho, A. C. P. L. F. (2020). An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Machine Learning. doi:10.1007/s10994-020-05879-3
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PFAHRINGER, Bernhard MYogarajan, V., Pfahringer, B., & Mayo, M. (2020). A review of automatic end-to-end de-Identification: Is high accuracy the only metric?. Applied Artificial Intelligence, 34(3), 251-269. doi:10.1080/08839514.2020.1718343
Yogarajan, V., Gouk, H., Smith, T., Mayo, M., & Pfahringer, B. (2020). Comparing high dimensional word embeddings trained on medical text to bag-of-words for predicting medical codes. In P. Sitek, M. Petranik, M. Krótkiewicz, & C. Srinilta (Eds.), Proc 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020) LNCS 12033 (pp. 97-108). Phuket, Thailand: Springer. doi:10.1007/978-3-030-41964-6_9
Open Access version at:
https://hdl.handle.net/10289/13591
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YOGARAJAN, Vithya :: SMITH, Anthony C (Tony) :: MAYO, Michael J :: PFAHRINGER, Bernhard MYogarajan, V., Montiel, J., Smith, T., & Pfahringer, B. (2020). Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes.. CoRR, abs/2004.00430.
Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Boosting decision stumps for dynamic feature selection on data streams. Information Systems, 83, 13-29. doi:10.1016/j.is.2019.02.003
Open Access version at:
https://hdl.handle.net/10289/12538
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BIFET FIGUEROL, Albert C :: GOMES, Heitor M :: PFAHRINGER, Bernhard MLeathart, T., Frank, E., Pfahringer, B., & Holmes, G. (2019). Ensembles of nested dichotomies with multiple subset evaluation. In Q. Yang, Z. -H. Zhou, Z. Gong, M. -L. Zhang, & S. -J. Huang (Eds.), Advances in Knowledge Discovery and Data Mining: Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), LNCS 11439 Vol. Part I (pp. 81-93). Cham: Springer. doi:10.1007/978-3-030-16148-4_7
Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2019). Towards automated configuration of stream clustering algorithms. In P. Cellier, & K. Driessens (Eds.), Machine Learning and Knowledge Discovery in Databases: Proc ECML PKDD 2019, Part 1 Vol. CCIS 1167 (pp. 137-143). Würzburg, Germany: Springer, Cham. doi:10.1007/978-3-030-43823-4_12
Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Merit-guided dynamic feature selection filter for data streams. Expert Systems with Applications, 116, 227-242. doi:10.1016/j.eswa.2018.09.031