| 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 | 25244 | 2009 |
| Correlation-based feature selection for machine learning MA Hall The University of Waikato, 1999 | 5138 | 1999 |
| Correlation-based feature selection of discrete and numeric class machine learning MA Hall University of Waikato, Department of Computer Science, 2000 | 2603 | 2000 |
| Weka: Practical machine learning tools and techniques with Java implementations IH Witten, E Frank, LE Trigg, MA Hall, G Holmes, SJ Cunningham | 2146* | 1999 |
| The WEKA workbench E Frank, MA Hall, IH Witten Morgan Kaufmann, 2016 | 1986 | 2016 |
| Logistic model trees N Landwehr, M Hall, E Frank Machine learning 59, 161-205, 2005 | 1677 | 2005 |
| Benchmarking attribute selection techniques for discrete class data mining MA Hall, G Holmes IEEE Transactions on Knowledge and Data engineering 15 (6), 1437-1447, 2003 | 1636 | 2003 |
| Correlation-based feature subset selection for machine learning MA Hall Thesis submitted in partial fulfilment of the requirements of the degree of …, 1988 | 1423 | 1988 |
| Data mining in bioinformatics using Weka E Frank, M Hall, L Trigg, G Holmes, IH Witten Bioinformatics 20 (15), 2479-2481, 2004 | 1164 | 2004 |
| Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper MA Hall, LA Smith Proceedings of the twelfth international Florida artificial intelligence …, 1999 | 844 | 1999 |
| A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form HL O’Brien, P Cairns, M Hall International Journal of Human-Computer Studies 112, 28-39, 2018 | 821 | 2018 |
| Flow clustering using machine learning techniques A McGregor, M Hall, P Lorier, J Brunskill Passive and Active Network Measurement: 5th International Workshop, PAM 2004 …, 2004 | 810 | 2004 |
| 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 | 807 | 2010 |
| A simple approach to ordinal classification E Frank, M Hall Machine Learning: ECML 2001: 12th European Conference on Machine Learning …, 2001 | 794 | 2001 |
| Practical feature subset selection for machine learning MA Hall, LA Smith Springer 20, 181-191, 1998 | 715 | 1998 |
| WEKA manual for version 3-9-1 RR Bouckaert, E Frank, M Hall, R Kirkby, P Reutemann, A Seewald, ... University of Waikato: Hamilton, New Zealand, 1-341, 2016 | 633* | 2016 |
| 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 | 534 | 2005 |
| Locally weighted naive bayes E Frank, M Hall, B Pfahringer arXiv preprint arXiv:1212.2487, 2012 | 496 | 2012 |
| 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 | 457 | 2010 |
| Generating rule sets from model trees G Holmes, M Hall, E Prank Advanced Topics in Artificial Intelligence: 12th Australian Joint Conference …, 1999 | 394 | 1999 |
Eibe FrankProfessor, Department of Computer Science, University of WaikatoVerified email at cs.waikato.ac.nz
