Eibe Frank
Eibe Frank
Professor, Department of Computer Science, University of Waikato
Verified email at cs.waikato.ac.nz - Homepage
Cited by
Cited by
Data Mining: Practical machine learning tools and techniques
IH Witten, E Frank, MA Hall, CJ Pal
Morgan Kaufmann, 2016
The WEKA data mining software: an update
M Hall, E Frank, G Holmes, B Pfahringer, P Reutemann, IH Witten
ACM SIGKDD Explorations Newsletter 11 (1), 10-18, 2009
Classifier chains for multi-label classification
J Read, B Pfahringer, G Holmes, E Frank
Machine learning 85 (3), 333-359, 2011
Generating accurate rule sets without global optimization
E Frank, IH Witten
International Conference on Machine Learning, 144-151, 1998
Logistic model trees
N Landwehr, M Hall, E Frank
Machine Learning 59 (1-2), 161-205, 2005
KEA: Practical Automated Keyphrase Extraction
IH Witten, GW Paynter, E Frank, C Gutwin, CG Nevill-Manning
Design and Usability of Digital Libraries: Case Studies in the Asia Pacific …, 2005
Domain-specific keyphrase extraction
E Frank, GW Paynter, IH Witten, C Gutwin, CG Nevill-Manning
Proceeding of 16th International Joint Conference on Artificial Intelligence …, 1999
The WEKA Workbench
E Frank, MA Hall, IH Witten
Data Mining: Practical Machine Learning Tools and Techniques, Online Appendix, 2016
Data mining in bioinformatics using Weka
E Frank, M Hall, L Trigg, G Holmes, IH Witten
Bioinformatics 20 (15), 2479-2481, 2004
Sentiment knowledge discovery in Twitter streaming data
A Bifet, E Frank
Discovery Science, 1-15, 2010
Weka: Practical machine learning tools and techniques with java implementations
IH Witten, E Frank, L Trigg, M Hall, G Holmes, SJ Cunningham
Proc ICONIP/ANZIIS/ANNES99 Future Directions for Intelligent Systems and …, 1999
Weka-a machine learning workbench for data mining
E Frank, M Hall, G Holmes, R Kirkby, B Pfahringer, IH Witten, L Trigg
Data Mining and Knowledge Discovery Handbook, 1269-1277, 2010
A simple approach to ordinal classification
E Frank, M Hall
European Conference on Machine Learning, 145-156, 2001
Using model trees for classification
E Frank, Y Wang, S Inglis, G Holmes, IH Witten
Machine Learning 32 (1), 63-76, 1998
Gene selection from microarray data for cancer classification--a machine learning approach
Y Wang, IV Tetko, MA Hall, E Frank, A Facius, KFX Mayer, HW Mewes
Computational Biology and Chemistry 29 (1), 37-46, 2005
Locally weighted naive Bayes
E Frank, M Hall, B Pfahringer
Proceedings of the Nineteenth conference on Uncertainty in Artificial …, 2002
WEKA---Experiences with a Java Open-Source Project
RR Bouckaert, E Frank, MA Hall, G Holmes, B Pfahringer, P Reutemann, ...
The Journal of Machine Learning Research 11, 2533-2541, 2010
Multinomial naive Bayes for text categorization revisited
AM Kibriya, E Frank, B Pfahringer, G Holmes
Lecture notes in computer science, 488-499, 2004
Generating rule sets from model trees
G Holmes, M Hall, E Frank
Advanced Topics in Artificial Intelligence, 1-12, 1999
A review of multi-instance learning assumptions
J Foulds, E Frank
The Knowledge Engineering Review 25 (1), 1-25, 2010
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