Professor Bernhard Pfahringer
Professor (Computer Science)
Qualifications: MEng PhD Vienna Tech
Contact Details
Email: bernhard@waikato.ac.nz
Room: G.1.24
Phone: +64 7 838 4041
Extension: 4041
Website: http://www.cs.waikato.ac.nz/~bernhard/
About Bernhard
Find out more about Bernhard and his work by viewing his professorial lecture: Dawn of the Age of Data: a Lecture to look at the data revolution (October 2015).
Research Interests
Data mining, machine learning, heuristic optimisation, programming languages.
Teaching Commitments
Recent Publications
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
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
Bravo-Marquez, F., Frank, E., Pfahringer, B., & Mohammad, S. M. (2019). AffectiveTweets: a Weka package for analyzing affect in tweets. Journal of Machine Learning Research, 20, 1-6. Retrieved from http://jmlr.org/papers/v20/18-450.html Open Access version: https://hdl.handle.net/10289/12617
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