Eibe Frank's Publications

R Alan Harris, Dorottya Nagy-Szakal, Sabina AV Mir, Eibe Frank, Reka Szigeti, Jess L Kaplan, Jiri Bronsky, Antone Opekun, George D Ferry, Harland Winter, and Richard Kellermayer. DNA methylation-associated colonic mucosal immune and defense responses in treatment-naïve pediatric ulcerative colitis. Epigenetics, 2014. [ bib | http ]

Eibe Frank. Fully supervised training of Gaussian radial basis function networks in WEKA. Technical Report 04/14, Department of Computer Science, University of Waikato, 2014. [ bib | .pdf ]

Michael Geilke, Eibe Frank, Andreas Karwath, and Stefan Kramer. Online estimation of discrete densities. In Proc 13th IEEE International Conference on Data Mining, Dallas, Texas. IEEE, 2013. [ bib | .pdf ]

Eibe Frank and Bernhard Pfahringer. Propositionalisation of multi-instance data using random forests. In Proc 26th Australasian Conference on Artificial Intelligence, Otago, New Zealand, pages 362-373. Springer, 2013. [ bib | .pdf ]

Claudia Kamphuis, Eibe Frank, Jennie K. Burke, Gwyn Verkerk, and Jenny Jago. Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness. Dairy Science, 2013. [ bib | http | .pdf ]

Jeremy Rajanayagam, Eibe Frank, Ross W. Shepherd, and Peter J. Lewindon. Artificial neural network is highly predictive of outcome in paediatric acute liver failure. Pediatric Transplantation, 2013. [ bib | http ]

Michael Geilke, Eibe Frank, and Stefan Kramer. Online estimation of discrete densities using classifier chains. In ECML PKDD 2012 Workshop on Instant Interactive Data Mining, Bristol, UK, 2012. [ bib | .pdf ]

Lan Huang, David Milne, Eibe Frank, and Ian H. Witten. Learning a concept-based document similarity measure. Journal of the American Society for Information Science and Technology, 2012. [ bib | .pdf ]

Albert Bifet, Eibe Frank, Geoffrey Holmes, and Bernhard Pfahringer. Ensembles of restricted Hoeffding trees. ACM Transactions on Intelligent Systems and Technology, 3(2), 2012. [ bib | .pdf ]

Luke Bjerring and Eibe Frank. Beyond trees: Adopting MITI to learn rules and ensemble classifiers for multi-instance data. In Proc 24th Australasian Joint Conference on Artificial Intelligence, Perth, Australia, pages 41-50. Springer, 2011. [ bib | .pdf ]

Michael Mayo and Eibe Frank. Experiments with multi-view multi-instance learning for supervised image classification. In Proc 26th International Conference Image and Vision Computing New Zealand, Auckland, New Zealand, pages 363-369, 2011. [ bib | .pdf ]

Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. Classifier chains for multi-label classification. Machine Learning, 85(3):333-359, 2011. [ bib | http | .pdf ]

Remco R. Bouckaert, Eibe Frank, Geoffrey Holmes, and Dale Fletcher. A comparison of methods for estimating prediction intervals in NIR spectroscopy: Size matters. Chemometrics and Intelligent Laboratory Systems, 109(2):139 - 145, 2011. [ bib | http | .pdf ]

Ian H. Witten, Eibe Frank, and Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA, 3 edition, 2011. [ bib | .html ]

Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. WEKA-experiences with a Java open-source project. Journal of Machine Learning Research, 11:2533-2541, 2010. [ bib | .pdf ]

Albert Bifet, Eibe Frank, Geoffrey Holmes, and Bernhard Pfahringer. Accurate ensembles for data streams: Combining restricted Hoeffding trees using stacking. In Proc 2nd Asian Conference on Machine Learning, Tokyo, pages 225-240. JMLR, 2010. [ bib | .pdf ]

James R. Foulds and Eibe Frank. Speeding up and boosting diverse density learning. In Proc 13th International Conference on Discovery Science, Canberra, Australia, pages 102-116. Springer, 2010. [ bib | .pdf ]

Albert Bifet and Eibe Frank. Sentiment knowledge discovery in Twitter streaming data. In Proc 13th International Conference on Discovery Science, Canberra, Australia, pages 1-15. Springer, 2010. [ bib | .pdf ]

Arthur Zimek, Fabian Buchwald, Eibe Frank, and Stefan Kramer. A study of hierarchical and flat classification of proteins. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 7:563-571, 2010. [ bib | http ]

Fabian Buchwald, Tobias Girschick, Eibe Frank, and Stefan Kramer. Fast conditional density estimation for quantitative structure-activity relationships. In Proc 24th AAAI Conference on Artificial Intelligence, 2010. [ bib | http ]

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Eibe Frank. Fast perceptron decision tree learning from evolving data streams. In Proc 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hyderabad, India, pages 299-310. Springer, 2010. [ bib | .pdf ]

James Foulds and Eibe Frank. A review of multi-instance learning assumptions. Knowledge Engineering Review, 25(1):1-25, 2010. [ bib | .pdf ]

Eibe Frank and Remco Bouckaert. Conditional density estimation with class probability estimators. In Proceedings of the 1st Asian Conference on Machine Learning, Nanjing, China, pages 65-81. Springer, 2009. [ bib | .pdf ]

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

Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. Classifier chains for multi-label classification. In Proc 13th European Conference on Principles and Practice of Knowledge Discovery in Databases and 20th European Conference on Machine Learning, Bled, Slovenia, pages 254-269. Springer, 2009. [ bib | .pdf ]

Olena Medelyan, Eibe Frank, and Ian H. Witten. Human-competitive tagging using automatic keyphrase extraction. In Proc Conf on Empirical Methods in Natural Language Processing, pages 1318-1327. ACL, 2009. [ bib | .pdf ]

Geoffrey Holmes, Dale Fletcher, Peter Reutemann, and Eibe Frank. Analysing chromatographic data using data mining to monitor petroleum content in water. In Proc 4th Int ICSC Symposium on Information Technologies in Environmental Engineering, pages 278-290. Springer, 2009. [ bib | .pdf ]

Anna Huang, David Milne, Eibe Frank, and Ian H. Witten. Clustering documents using a Wikipedia-based concept representation. In Proc 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkog, Thailand, pages 628-636. Springer, 2009. [ bib | .pdf ]

Martin Gütlein, Eibe Frank, Mark Hall, and Andreas Karwath. Large-scale attribute selection using wrappers. In Proc IEEE Symposium on Computational Intelligence and Data Mining, pages 332-339. IEEE, 2009. [ bib | .pdf ]

Arie Ben-David and Eibe Frank. Accuracy of machine learning models versus "hand crafted" expert systems - a credit scoring case study. Expert Systems with Applications, 36(3):5264-5271, 2009. [ bib | http ]

Anna Huang, David Milne, Eibe Frank, and Ian H. Witten. Clustering documents with active learning using Wikipedia. In Proc 8th IEEE International Conference on Data Mining, Pisa, Italy, pages 839-844. IEEE Computer Society, 2008. [ bib | .pdf ]

James Foulds and Eibe Frank. Revisiting multiple-instance learning via embedded instance selection. In Proc 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, pages 300-310. Springer, 2008. [ bib | .pdf ]

Eibe Frank and Mark Hall. Additive regression applied to a large-scale collaborative filtering problem. In Proc 21set Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, pages 435-446. Springer, 2008. [ bib | .pdf ]

Kathryn Hempstalk and Eibe Frank. Discriminating against new classes: One-class versus multi-class classification. In Proc 21set Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, pages 325-336. Springer, 2008. [ bib | .pdf ]

Kathryn Hempstalk, Eibe Frank, and Ian H. Witten. One-class classification by combining density and class probability estimation. In Proc 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, Antwerp, Belgium, pages 505-519. Springer, 2008. [ bib | .pdf ]

Mark Hall and Eibe Frank. Combining naive Bayes and decision tables. In Proc 21st Florida Artificial Intelligence Research Society Conference, Miami, Florida, pages 318-319. AAAI Press, 2008. [ bib | .pdf ]

Ashraf M. Kibriya and Eibe Frank. An empirical comparison of exact nearest neighbour algorithms. In Proc 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, pages 140-151. Springer, 2007. [ bib | .pdf ]

Eibe Frank and Remco R. Bouckaert. Naive Bayes for text classification with unbalanced classes. In Proc 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, pages 503-510. Springer, 2006. [ bib | .pdf ]

Eibe Frank and Bernhard Pfahringer. Improving on bagging with input smearing. In Proc 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore, pages 97-106. Springer, 2006. [ bib | .ps.gz | .pdf ]

Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco, 2 edition, 2005. [ bib | .html ]

Niels Landwehr, Mark Hall, and Eibe Frank. Logistic model trees. Machine Learning, 59(1-2):161-205, 2005. [ bib | .ps.gz | .pdf ]

Yu Wang, Igor V. Tetko, Mark A. Hall, Eibe Frank, Axel Facius, Klaus F. X. Mayer, and Hans-Werner Mewes. Gene selection from microarray data for cancer classification - a machine learning approach. Computational Biology and Chemistry, 29(1):37-46, 2005. [ bib | http ]

Eibe Frank, Mark A. Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer, Ian H. Witten, and Len Trigg. Weka - a machine learning workbench for data mining. In Oded Maimon and Lior Rokach, editors, The Data Mining and Knowledge Discovery Handbook, pages 1305-1314. Springer, 2005. [ bib | .ps.gz | .pdf ]

Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin, and Craig G. Nevill-Manning. Kea: Practical automatic keyphrase extraction. In Y.-L. Theng and S. Foo, editors, Design and Usability of Digital Libraries: Case Studies in the Asia Pacific, pages 129-152. Information Science Publishing, London, 2005. [ bib | .ps.gz | .pdf ]

Gabi Schmidberger and Eibe Frank. Unsupervised discretization using tree-based density estimation. In Proc 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, pages 240-251. Springer, 2005. [ bib | .ps.gz | .pdf ]

Lin Dong, Eibe Frank, and Stefan Kramer. Ensembles of balanced nested dichotomies for multi-class problems. In Proc 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, pages 84-95. Springer, 2005. [ bib | .ps.gz | .pdf ]

Marc Sumner, Eibe Frank, and Mark A. Hall. Speeding up logistic model tree induction. In Proc 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, pages 675-683. Springer, 2005. [ bib | .ps.gz | .pdf ]

Peter Reutemann, Bernhard Pfahringer, and Eibe Frank. A toolbox for learning from relational data with propositional and multi-instance learners. In Proc 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, pages 1017-1023. Springer, 2004. [ bib | .ps.gz | .pdf ]

Ashraf M. Kibriya, Eibe Frank, Bernhard Pfahringer, and Geoffrey Holmes. Multinomial naive Bayes for text categorization revisited. In Proc 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, pages 488-499. Springer, 2004. [ bib | .ps.gz | .pdf ]

Stefan Mutter, Mark Hall, and Eibe Frank. Using classification to evaluate the output of confidence-based association rule mining. In Proc 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, pages 538-549. Springer, 2004. [ bib | .ps.gz | .pdf ]

Eibe Frank and Stefan Kramer. Ensembles of nested dichotomies for multi-class problems. In Proc 21st International Conference on Machine Learning, Banff, Canada, pages 305-312. ACM Press, 2004. [ bib | .ps.gz | .pdf ]

Xin Xu and Eibe Frank. Logistic regression and boosting for labeled bags of instances. In Proc 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia, pages 272-281. Springer, 2004. [ bib | .ps.gz | .pdf ]

Remco R. Bouckaert and Eibe Frank. Evaluating the replicability of significance tests for comparing learning algorithms. In Proc 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia, pages 3-12. Springer, 2004. [ bib | .ps.gz | .pdf ]

Eibe Frank, Mark Hall, Leonard E. Trigg, Geoffrey Holmes, and Ian H. Witten. Data mining in bioinformatics using Weka. Bioinformatics, 20(15):2479-2481, 2004. [ bib | .ps.gz | .pdf ]

Eibe Frank and Gordon W. Paynter. Predicting library of congress classifications from library of congress subject headings. JASIST, 55(3):214-227, 2004. [ bib | .pdf ]

Niels Landwehr, Mark Hall, and Eibe Frank. Logistic model trees. In Proc 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, pages 241-252. Springer, 2003. [ bib | .ps.gz | .pdf ]

Nils Weidmann, Eibe Frank, and Bernhard Pfahringer. A two-level learning method for generalized multi-instance problems. In Proc 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, pages 468-479. Springer, 2003. [ bib | .ps.gz | .pdf ]

Eibe Frank and Mark Hall. Visualizing class probability estimators. In Proc 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 168-179. Springer, 2003. [ bib | .ps.gz | .pdf ]

Eibe Frank, Mark Hall, and Bernhard Pfahringer. Locally weighted naive Bayes. In Proc 19th Conference in Uncertainty in Artificial Intelligence, Acapulco, Mexico, pages 249-256. Morgan Kaufmann, 2003. [ bib | .ps.gz | .pdf ]

Eibe Frank and Xin Xu. Applying propositional learning algorithms to multi-instance data. Technical Report 06/03, Department of Computer Science, University of Waikato, 2003. [ bib | .ps.gz | .pdf ]

Stefan Kramer, Eibe Frank, and Christoph Helma. Fragment generation and support vector machines for inducing SARs. SAR and QSAR in Environmental Research, 13(5):509-523, 2002. [ bib | http ]

Eibe Frank, Geoffrey Holmes, Richard Kirkby, and Mark Hall. Racing committees for large datasets. In Proc 5th International Conference on Discovery Science, pages 153-164. Springer, 2002. [ bib | .ps.gz | .pdf ]

Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, and Mark Hall. Multiclass alternating decision trees. In Proc 13th European Conference on Machine Learning, Helsinki, Finland, pages 161-172. Springer, 2002. [ bib | .ps.gz | .pdf ]

Eibe Frank and Mark Hall. A simple approach to ordinal classification. Technical Report 01/05, Department of Computer Science, University of Waikato, 2001. [ bib | .ps.gz | .pdf ]

Eibe Frank and Mark Hall. A simple approach to ordinal classification. In Proc 12th European Conference on Machine Learning, Freiburg, Germany, pages 145-156. Springer, 2001. Note: there is a small bug in the description of the algorithm. Please consult [64] instead. [ bib ]

Andrew Turpin, Eibe Frank, Mark Hall, Ian H. Witten, and Chris A. Johnson. Determining progression in glaucoma using visual fields. In Proc 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hong Kong, China, pages 136-147. Springer, 2001. [ bib | .ps.gz | .pdf ]

Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, and Ian H. Witten. Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud., 55(3):281-292, 2001. [ bib | .ps.gz | .pdf ]

Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, 2000. [ bib | .html ]

Chang Chui Eibe Frank and Ian H. Witten. Text categorization using compression models. Technical Report 00/02, Department of Computer Science, University of Waikato, 2000. [ bib | .ps.gz | .pdf ]

Eibe Frank, Chang Chui, and Ian H. Witten. Text categorization using compression models. In Data Compression Conference, Snowbird, Utah, page 555. IEEE Computer Society, 2000. Note: abstract only. Full paper is available as [69]. [ bib | .ps.gz | .pdf ]

Stefan Kramer and Eibe Frank. Bottom-up propositionalization. In Proc Work-in-progress reports of the 10th International Conference on Inductive Logic Programming. CEUR-WS.org, 2000. [ bib | .ps.gz | .pdf ]

Eibe Frank. Pruning Decision Trees and Lists. PhD thesis, Department of Computer Science, University of Waikato, 2000. [ bib | .ps.gz | .pdf ]

Eibe Frank, Leonard E. Trigg, Geoffrey Holmes, and Ian H. Witten. Naive Bayes for regression (technical note). Machine Learning, 41(1):5-25, 2000. [ bib | .ps.gz | .pdf ]

Carl Gutwin, Gordon W. Paynter, Ian H. Witten, Craig G. Nevill-Manning, and Eibe Frank. Improving browsing in digital libraries with keyphrase indexes. Decision Support Systems, 27(1/2):81-104, 1999. [ bib | .ps.gz ]

Sally Jo Cunningham and Eibe Frank. Market basket analysis of library circulation data. In Proc 6th International Conference on Neural Information Processing, Perth, Australia, pages 825-830. IEEE Service Center, 1999. [ bib | .ps.gz | .pdf ]

Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin, and Craig G. Nevill-Manning. KEA: Practical automatic keyphrase extraction. In Proc 4th ACM conference on Digital Libraries, Berkeley, CA, pages 254-255. ACM, 1999. [ bib | http ]

Geoffrey Holmes, Mark Hall, and Eibe Frank. Generating rule sets from model trees. In Proc 12th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, pages 1-12. Springer, 1999. [ bib | .ps.gz | .pdf ]

Eibe Frank and Ian H. Witten. Making better use of global discretization. In Proc 16th International Conference on Machine Learning, Bled, Slovenia, pages 115-123. Morgan Kaufmann, 1999. [ bib | .ps.gz | .pdf ]

Eibe Frank, Gordon W. Paynter, Ian H. Witten, Carl Gutwin, and Craig G. Nevill-Manning. Domain-specific keyphrase extraction. In Proc 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pages 668-673. Morgan Kaufmann, 1999. [ bib | .ps.gz | .pdf ]

Ian H. Witten, Eibe Frank, Len Trigg, Mark Hall, Geoffrey Holmes, and Sally Jo Cunningham. Weka: Practical machine learning tools and techniques with Java implementations. In Nikola Kasabov and Kitty Ko, editors, Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems, pages 192-196, 1999. Dunedin, New Zealand. [ bib | .ps.gz | .pdf ]

Eibe Frank and Ian H. Witten. Generating accurate rule sets without global optimization. In Proc 15th International Conference on Machine Learning, Madison, Wisconsin, pages 144-151. Morgan Kaufmann, 1998. [ bib | .ps.gz | .pdf ]

Eibe Frank and Ian H. Witten. Using a permutation test for attribute selection in decision trees. In Proc 15th International Conference on Machine Learning, Madison, Wisconsin, pages 152-160. Morgan Kaufmann, 1998. [ bib | .ps.gz | .pdf ]

Eibe Frank, Yong Wang, Stuart Inglis, Geoffrey Holmes, and Ian H. Witten. Using model trees for classification. Machine Learning, 32(1):63-76, 1998. [ bib | .ps.gz | .pdf ]

Eibe Frank and Ian H. Witten. Selecting multiway splits in decision trees. Technical Report 96/31, Department of Computer Science, University of Waikato, 1996. [ bib | .ps.gz | .pdf ]

Eibe Frank and Klaus-Peter Huber. Active learning of soft rules for system modelling. In Proc 2nd European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, pages 1430-1434, Aachen, 1996. Verlag Mainz. [ bib | .ps.gz | .pdf ]


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