PUBLICATIONS

Books:

Authored
Ian H. Witten, Eibe Frank, and Mark A. Hall. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA, 3 edition.

Hall, M. A. (1999). Correlation-based Feature Subset Selection for Machine Learning. PhD dissertation, Department of Computer Science, University of Waikato.

Book Chapter
Frank, E., Hall, M. Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H. and Trigg, L. (2005). Weka: A Machine Learning Workbench for Data Mining.Maimon, O. and Rokach, L. (eds). Data Mining and Knowledge Discovery Handbook, Chapter 62, 1305-1314. Springer-Verlag.

Hall, M. A. (1999). Correlation-based Feature Subset Selection for Machine Learning. PhD dissertation, Department of Computer Science, University of Waikato.

Journal Articles:

Article in Refereed Journal
Remco R. Bouckaert, Eibe Frank, Mark A. Hall. Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. (2010). WEKA-experiences with a java open-source project. Journal of Machine Learning Research, 11:2533-2541.

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11(1):10-18.

Hall, M. (2006). A Decision Tree-Based Attribute Weighting Filter for Naive Bayes. Knowledge Based Systems, 20(2), 120-126.

Landwehr, N., Hall, M. and Frank, E. (2005). Logistic Model Trees. Machine Learning. 59(1/2), 161-205.

Frank, E., Hall, M., Trigg, L., Holmes, G. and Witten, I.H. (2004). Data Mining in Bioinformatics using Weka. Bioinformatics, 20(15), 2479-2481.

Hall, M. and Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 15(3), November/December 2003.

Holmes, G. and Hall, M. (2002). A development Environment for predictive modelling in foods. International Journal of Food Microbiology, 73, 351-362.

Ware, M., Frank E., Holmes G., Hall M. and Witten I.H. (2001). Interactive machine learning--letting users build classifiers. International Journal of Human-Computer Studies. 55(3), 281-292.

Hall, M. A., Smith, L. (1996). A computer model of blues music and its evaluation. Journal of the Acoustical Society of America, 100(2), 1163-1167.

Conference Proceedings:

Full Written Paper
M. Gutlein, Frank E. and Hall, M. (2009). Large-scale attribute selection using wrappers. Proc IEEE Symposium on Computational Intelligence and Data Mining. IEEE. 332-339.

Frank, E. and Hall, M. (2008). Additive regression applied to a large-scale collaborative filtering problem. Proc 21set Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand. Springer.

Hall, M. and Frank, E. (2008). Combining naive Bayes and decision tables. Proc 21st Florida Artificial Intelligence Research Society Conference, Miami, Florida. AAAI Press.

Hall, M. (2006). A Decision Tree-Based Attribute Weighting Filter for Naive Bayes. SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer-Verlag. 59-70.

Sumner, M., Frank, E. and Hall, M. (2005). Speeding up logistic model tree induction. European Conference on Principles and Practice of Knowledge Discovery in Databases. Porto, Portugal. Springer-Verlag. 675-683.

Mutter, S., Hall, M. and Frank, E. (2004). Using classification to evaluate the output of confidence-based association rule mining. Australian Joint Conference on Artificial Intelligence, Cairns, Australia. Springer-Verlag. 538-549.

Frank, E. and Hall, M. (2003). Visualizing class probability estimators. European Conference on Principles and Practice of Knowledge Discovery in Databases. Cavtat-Dubrovnik, Croatia. Springer-Verlag. 168-179.

Frank, E., Hall, M. and Pfahringer, B. (2003). Locally weighted naive Bayes. Conference on Uncertainty in Artificial Intelligence. Acapulco, Mexico. Morgan Kaufmann. 249-256.

Landwehr, N., Hall, M. and Frank E. (2003). Logistic model trees. European Conference on Machine Learning. Cavtat-Dubrovnik, Croatia. Springer-Verlag. 241-252.

Eibe Frank, Geoffrey Holmes, Richard Kirkby, and Mark Hall (2002). Racing Committees for Large Datasets. International Conference on Discovery Science, Lübeck, Germany. Springer-Verlag. 153-164.

Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank and Mark Hall (2002). Multiclass Alternating Decision Trees. European Conference on Machine Learning, Helsinki, Finland. Springer-Verlag. 161-172.

Hall, M., Kusabs, N. J., Gillgren, D. and Bollen, A. F. (2001). Investigation of association models to describe consumer purchase patterns. International Symposium on Application of Modeling as an Innovative Technology in the Agri-Food-Chain. Palmerston North, New Zealand. ISHS. 167-173.

Frank, E. and Hall, M. (2001). A simple approach to ordinal prediction. European Conference on Machine Learning. Freiburg, Germany. Springer-Verlag. 145-156.
(An updated version of this paper is available as Working Paper 01/05, Department of Computer Science, University of Waikato.)

Turpin, A., Frank, E., Hall, M., Witten, I. H. and Johnson, C. A. (2001). Determining progression in glaucoma using visual fields. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Hong Kong. Springer-Verlag. 136-147.

Cleary, J. G., Trigg, L. E., Holmes, G. and Hall, M. A. (2000). Experiences with a weighted decision tree learner. SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence. Cambridge, England. Springer-Verlag. 35-47.

Bollen, A. F., Kusabs, N. J., Holmes, G., and Hall, M. A. (2000) Comparison of consumer and producer perceptions of mushroom quality. Integrated View of Fruit and Vegetable Quality: Proceedings of Multidisciplinary Conference, edited by W. J. Florkowski, S. E. Prussia and R. L. Shewfelt, Georgia, USA. 303-311.

Hall, M. A. (2000). Correlation-based feature selection for discrete and numeric class machine learning. International Conference on Machine Learning, Stanford University, CA. Morgan Kaufmann Publishers. 359-366.

Holmes, G., Hall, M. A., Frank, E. (1999). Generating rule sets from model trees. Australian Joint Conference on Artificial Intelligence. Sydney, Australia. Springer Verlag. pp 1-12.

Hall, M. A., Smith, L. A. (1999). Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. Florida Artificial Intelligence Symposium. AAAI Press. 235-239.

Hall, M. A., Smith, L. A. (1998). Practical feature subset selection for machine learning. Australian Computer Science Conference. Springer. 181-191.

Hall, M. A., Smith, L. A. (1997). Feature subset selection: a correlation based filter approach. International Conference on Neural Information Processing and Intelligent Information Systems. Springer. 855-858.

Hall, M. A. (1995). Selection of attributes for modeling Bach chorales by a genetic algorithm. New Zealand Two-Stream Conference on Artificial Neural Networks and Expert Systems. Dunedin, NZ. IEEE Computer Society Press. 182-185.

Hall, M. A. (1992). Evaluating models of music. Proceedings of the First New Zealand Computer Science Research Students' Conference..