Research Publications for Bernhard M Pfahringer
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Yogarajan, 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
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., 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 MPfahringer, 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
Yogarajan, 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.
Leathart, T., Frank, E., Pfahringer, B., & Holmes, G. (2019). On calibration of nested dichotomies. 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. 69-80). Cham: Springer. doi:10.1007/978-3-030-16148-4_6
Wicker, J., Hua, Y. C., Rebello, R., & Pfahringer, B. (2019). XOR-Based boolean matrix decomposition. In Proceedings - IEEE International Conference on Data Mining, ICDM Vol. 2019-November (pp. 638-647). doi:10.1109/ICDM.2019.00074
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PFAHRINGER, Bernhard MGomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfahringer, B., . . . Abdessalem, T. (2019). Correction to: Adaptive random forests for evolving data stream classification (Machine Learning, (2017), 106, 9-10, (1469-1495), 10.1007/s10994-017-5642-8). Machine Learning, 108, 1877-1878. doi:10.1007/s10994-019-05793-3
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BIFET FIGUEROL, Albert C :: GOMES, Heitor M :: PFAHRINGER, Bernhard M :: HOLMES, GeoffreyGomes, H. M., Mello, R. F. D., Pfahringer, B., & Bifet, A. (2019). Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 761-769). doi:10.1109/BigData47090.2019.9006366