Professor Bernhard Pfahringer
Dipl.-Ing. TU Wien, Dr.techn. TU Wien
Bernhard received his Dr. techn. (~ PhD) degree from the University of Technology in Vienna, Austria, in 1995. In Vienna he was a research fellow with the Austrian Research Institute for Artificial Intelligence from 1985 to 1999. From 2000 until May 2017 he was affiliated with the Department of Computer Science at the University of Waikato, going from Senior Lecturer to Professor, and also serving as the Head of Department for about 4.5 years. He joined the University of Auckland in June 2017.
Research | Current
I am fascinated by analysing data, uncovering useful information and knowledge. I love developing and improving algorithms for this purpose. My research interests span a range of data mining and machine learning sub-fields, with a current focus on streaming, randomization, and complex data.
Selected publications and creative works (Research Outputs)
- Barddal, J. P., Gomes, H. M., Enembreck, F., & Pfahringer, B. (2017). A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. Journal of Systems and Software, 127, 278-294. 10.1016/j.jss.2016.07.005
- Bravo-Marquez, F., Frank, E., Mohammad, S. M., & Pfahringer, B. (2017). Determining Word-Emotion Associations from Tweets by Multi-label Classification. Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016. 10.1109/WI.2016.0091
- Bravo-Marquez, F., Frank, E., & Pfahringer, B. (2017). From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach. Proceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016. 10.1109/WI.2016.0030
- Bravo-Marquez, F., Frank, E., & Pfahringer, B. (2016). Building a Twitter opinion lexicon from automatically-annotated tweets. Knowledge-Based Systems, 108, 65-78. 10.1016/j.knosys.2016.05.018
- Read, J., Reutemann, P., Pfahringer, B., & Holmes, G. (2016). MEKA: A Multi-label/Multi-target Extension to WEKA. JOURNAL OF MACHINE LEARNING RESEARCH, 17
- Barddal, J. P., Gomes, H. M., Enembreck, F., Pfahringer, B., & Bifet, A. (2016). On dynamic feature weighting for feature drifting data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10.1007/978-3-319-46227-1_9
- Bifet, A., De Francisci Morales, G., Read, J., Holmes, G., & Pfahringer, B. (2015). Efficient online evaluation of big data stream classifiers. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 10.1145/2783258.2783372
- Torgo, L., Branco, P., Ribeiro, R. P., & Pfahringer, B. (2015). Resampling strategies for regression. Expert Systems, 32 (3), 465-476. 10.1111/exsy.12081