Hongyu Wang, Henry Gouk, Huon Fraser, Eibe Frank, Bernhard Pfahringer, Michael Mayo, and Geoffrey Holmes. Experiments in cross-domain few-shot learning for image classification. Journal of the Royal Society of New Zealand, 53(1):169--191, 2023. [ bib | pdf ]
Nick Lim, Albert Bifet, Daniel Bull, Eibe Frank, Yunzhe Jia, Jacob Montiel, and Bernhard Pfahringer. Showcasing the TAIAO project: providing resources for machine learning from images of New Zealand's natural environment. Journal of the Royal Society of New Zealand, 53(1):69--81, 2023. [ bib | pdf ]
Mi Li, Eibe Frank, and Bernhard Pfahringer. Large scale K-means clustering using GPUs. Data Mining and Knowledge Discovery, 37:67--109, 2023. [ bib | pdf ]
Attaullah Sahito, Eibe Frank, and Bernhard Pfahringer. Better self-training for image classification through self-supervision. In Proc 34th Australasian Joint Conference on Artificial Intelligence, Sydney, Australia, pages 645--657. Springer, 2022. [ bib | pdf ]
Rory Mitchell, Joshua Cooper, Eibe Frank, and Geoffrey Holmes. Sampling permutations for Shapley value estimation. Journal of Machine Learning Research, 23(43):1--46, 2022. [ bib | pdf ]
Rory Mitchell, Eibe Frank, and Geoffrey Holmes. GPUTreeShap: Massively parallel exact calculation of SHAP scores for tree ensembles. PeerJ CS, 8:e880, 2022. [ bib | pdf ]
Julia Falconer, Eibe Frank, Devon Polaschek, and Chaitanya Joshi. Methods for eliciting informative prior distributions: A critical review. Decision Analysis, 19(3):189--2004, 2022. [ bib | pdf ]
Geoff Holmes, Eibe Frank, Dale Fletcher, and Corey Sterling. Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models. In Proc 27th International Conference on Intelligent User Interfaces, Helsinki, Finland, pages 584--593. ACM, 2022. [ bib | pdf ]
Rory Mitchell, Daniel Stokes, Eibe Frank, and Geoffrey Holmes. Bandwidth-optimal random shuffling for GPUs. ACM Transactions on Parallel Computing, 9(1):1--20, 2022. [ bib | pdf ]
Zac Pullar-Strecker, Katharina Dost, Eibe Frank, and Jörg Wicker. Hitting the target: stopping active learning at the cost-based optimum. Machine Learning, 2022. [ bib | pdf ]
Hongyu Wang, Huon Fraser, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo, and Geoff Holmes. Experiments in cross-domain few-shot learning for image classification: Extended abstract. In Pavel Brazdil, Jan N. van Rijn, Henry Gouk, and Felix Mohr, editors, ECMLPKDD Workshop on Meta-Knowledge Transfer, volume 191 of Proceedings of Machine Learning Research, pages 81--83. PMLR, 2022. [ bib | pdf | .html ]
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J. Cree. Regularisation of neural networks by enforcing Lipschitz continuity. Machine Learning, 110(2):393--416, 2021. [ bib | http ]
Jesse Read, Bernhard Pfahringer, Geoffrey Holmes, and Eibe Frank. Classifier chains: A review and perspectives. Journal of Artificial Intelligence Research, 70:683--718, 2021. [ bib | http ]
Rory Mitchell, Eibe Frank, and Geoffrey Holmes. An empirical study of moment estimators for quantile approximation. ACM Transactions on Database Systems, 46(1):3:1--3:21, 2021. [ bib | .pdf ]
Yunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet, and Nick Lim. Studying and exploiting the relationship between model accuracy and explanation quality. In Proc 25nd European Conference on Principles and Practice of Knowledge Discovery in Databases and 29th European Conference on Machine Learning, Bilbao, Basque Country, Spain, pages 699--714. Springer, 2021. [ bib | .pdf ]
Moi Hoon Yap, Ryo Hachiuma, Azadeh Alavi, Raphael Brüngel, Bill Cassidy, Manu Goyal, Hongtao Zhu, Johannes Rückert, Moshe Olshansky, Xiao Huang, Hideo Saito, Saeed Hassanpour, Christoph M. Friedrich, David Ascher, Anping Song, Hiroki Kajita, David Gillespie, Neil D. Reeves, Joseph Pappachan, Claire O'Shea, and Eibe Frank. Deep learning in diabetic foot ulcers detection: A comprehensive evaluation. Computers in Biology and Medicine, 135:104596, 2021. [ bib | http ]
Bill Cassidy, Neil D. Reeves, Joseph M. Pappachan, David Gillespie, Claire O'Shea, Satyan Rajbhandari, Arun G. Maiya, Eibe Frank, Andrew J. M. Boulton, David G. Armstrong, Bijan Najafi, Justina Wu, Rupinder Singh Kochhar, and Moi Hoon Yap. The dfuc 2020 dataset: Analysis towards diabetic foot ulcer detection. touchREVIEWS in Endocrinology, 17(1):5--11, 2021. [ bib | http ]
Daniel Bull, Nick Lim, and Eibe Frank. Perceptual improvements for super-resolution of satellite imagery. In Proc 36th International Conference on Image and Vision Computing New Zealand, pages 1--6. IEEE, 2021. [ bib | pdf ]
Michael Mayo and Eibe Frank. Improving naive Bayes for regression with optimized artificial surrogate data. Applied Artificial Intelligence, 34(6):484--514, 2020. [ bib | pdf ]
Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, and Albert Bifet. Adaptive XGBoost for evolving data streams. In Proc International Joint Conference on Neural Networks, Glasgow, UK, pages 1--8. IEEE, 2020. [ bib | pdf ]
Rhys Compton, Eibe Frank, Panos Patros, and Abigail Koay. Embedding java classes with code2vec: Improvements from variable obfuscation. In Proc 17th International Conference on Mining Software Repositories, Seoul, Republic of Korea, pages 243--253. ACM Press, 2020. [ bib | pdf ]
Hongyu Wang, Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael Mayo. A comparison of machine learning methods for cross-domain few-shot learning. In Proc 33rd Australasian Joint Conference on Artificial Intelligence, Canberra, Australia, pages 445--457. Springer, 2020. [ bib | pdf ]
Attaullah Sahito, Eibe Frank, and Bernhard Pfahringer. Transfer of pretrained model weights substantially improves semi-supervised image classification. In Proc 33rd Australasian Joint Conference on Artificial Intelligence, Canberra, Australia, pages 433--444. Springer, 2020. [ bib | pdf ]
Sheldon Coup, Varvara Vetrova, and Rachael Tappenden. Domain specific transfer learning using image mixing and stochastic image selection. In Proc 6th Workshop on Fine-Grained Visual Categorization, Computer Vision and Pattern Recognition Conference, Long Beach, CA, 2019. [ bib | pdf ]
Tim Leathart, Eibe Frank, Bernhard Pfahringer, and Geoffrey Holmes. Ensembles of nested dichotomies with multiple subset evaluation. In Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Macau, China, pages 81--93. Springer, 2019. [ bib | pdf | supplementary material ]
Tim Leathart, Eibe Frank, Bernhard Pfahringer, and Geoffrey Holmes. On calibration of nested dichotomies. In Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Macau, China, pages 69--80. Springer, 2019. [ bib | pdf ]
Steven Lang, Felipe Bravo-Marquez, Christopher Beckham, Mark Hall, and Eibe Frank. WekaDeeplearning4j: a deep learning package for Weka based on DeepLearning4j. Knowledge-Based Systems, 178, 2019. [ bib | pdf ]
Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, and Saif M. Mohammad. AffectiveTweets: a Weka package for analyzing affect in tweets. Journal of Machine Learning Research, 20(92):1--6, 2019. [ bib | pdf ]
Henry Gouk, Bernhard Pfahringer, and Eibe Frank. Stochastic gradient trees. In Proc 11th Asian Conference on Machine Learning, Nagoya, Japan, pages 1094--1109. PMLR, 2019. [ bib | pdf ]
Attaullah Sahito, Eibe Frank, and Bernhard Pfahringer. Semi-supervised learning using Siamese networks. In Proc 32nd Australasian Joint Conference on Artificial Intelligence, Adelaide, Australia, pages 586--597. Springer, 2019. [ bib | pdf ]
Iakovos Gurulian, Konstantinos Markantonakis, Eibe Frank, and Raja Naeem Akram. Good vibrations: Artificial ambience-based relay attack detection. In Proc 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, New York, US, pages 481--489. IEEE, 2018. [ bib | pdf ]
Varvara Vetrova, Sheldon Coup, Eibe Frank, and Michael Cree. Difference in details: transfer learning case study of "cryptic" plants and moths. In The 5th Workshop on Fine-Grained Visual Categorization held in conjunction with CVPR 2018, Salt Lake City, UT, 2018. [ bib | pdf ]
Kohji Muraoka, Paul Hanson, Eibe Frank, Meilan Jiang, Kenneth Chiu, and David Hamilton. A data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysis. Limnology and Oceanography: Methods, 16(11):787--801, 2018. [ bib | pdf ]
Varvara Vetrova, Sheldon Coup, Eibe Frank, and Michael J. Cree. Hidden features: Experiments with feature transfer for fine-grained multi-class and one-class image categorization. In Proc 33rd International Conference on Image and Vision Computing New Zealand, Auckland, New Zealand, pages 1--6. IEEE CPS, 2018. [ bib | pdf ]
Felipe Bravo-Marquez, Eibe Frank, and Bernhard Pfahringer. Transferring sentiment knowledge between words and tweets. Web Intelligence, 16(4):203--220, 2018. [ bib | pdf ]
Henry Gouk, Bernhard Pfahringer, Eibe Frank, and Michael J. Cree. MaxGain: Regularisation of neural networks by constraining activation magnitudes. In Proc 22nd European Conference on Principles and Practice of Knowledge Discovery in Databases and 29th European Conference on Machine Learning, Dublin, Ireland, pages 541--556. Springer, 2018. [ bib | pdf ]
Carlton Shepherd, Iakovos Gurulian, Konstantinos Markantonakis, Eibe Frank, Raja Naeem Akram, Emmanouil Panaousis, and Keith Mayes. The applicability of ambient sensors as proximity evidence for NFC transactions. In Proc 6th Workshop on Mobile Security Technologies (MoST), San Jose, United States, pages 179--188. IEEE Computer Society's Technical Committee on Security and Privacy, 2017. [ bib | .pdf ]
Iakovos Gurulian, Carlton Shepherd, Eibe Frank, Konstantinos Markantonakis, Raja Naeem Akram, and Keith Mayes. On the effectiveness of ambient sensing for detecting NFC relay attacks. In Proc 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Sydney, Australia, pages 41--49. IEEE, 2017. [ bib | pdf ]
Jeff Mo, Eibe Frank, and Varvara Vetrova. Large-scale automatic species identification. In Proc 30th Australasian Joint Conference on Artificial Intelligence, Melbourne, Australia, pages 301--312. Springer, 2017. [ bib | pdf ]
Rory Mitchell and Eibe Frank. Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3(e127), 2017. [ bib | http ]
Iakovos Gurulian, Konstantinos Markantonakis, Carlton Shepherd, Eibe Frank, and Raja Naeem Akram. Proximity assurances based on natural and artificial ambient environments. In Proc 10th International Conference on Information Technology and Communications Security, Bucharest, Romania, pages 83--103. Springer, 2017. [ bib | pdf ]
Michael Geilke, Andreas Karwath, Eibe Frank, and Stefan Kramer. Online estimation of discrete, continuous, and conditional joint densities using classifier chains. Data Mining and Knowledge Discovery, 32(3):561--603, 2017. [ bib | pdf ]
Paula Branco, Luís Torgo, Rita P. Ribeiro, Eibe Frank, Bernhard Pfahringer, and Markus Michael Rau. Learning through utility optimization in regression tasks. In Proc 4th IEEE International Conference on Data Science and Advanced Analytics, Tokyo, Japan, pages 30--39. IEEE, 2017. [ bib | pdf ]
Tim Leathart, Eibe Frank, Geoffrey Holmes, and Bernhard Pfahringer. Probability calibration trees. In Proc 9th Asian Conference on Machine Learning, Seoul, Korea, pages 145--160. Proceedings of Machine Learning Research, 2017. [ bib | pdf ]
Tony C. Smith and Eibe Frank. Introducing machine learning concepts with weka. In Statistical Genomics: Methods and Protocols, pages 353--378. Springer, New York, NY, 2016. [ bib | pdf ]
Felipe Bravo-Marquez, Eibe Frank, and Bernhard Pfahringer. Building a Twitter opinion lexicon from automatically-annotated tweets. Knowledge-Based Systems, 108:65--78, 2016. [ bib | .pdf ]
Felipe Bravo-Marquez, Eibe Frank, and Bernhard Pfahringer. Annotate-sample-average (ASA): A new distant supervision approach for Twitter sentiment analysis. In Proc 22nd European Conference on Artificial Intelligence, The Hague, Netherlands, pages 498--506. IOS Press, 2016. [ bib | pdf ]
Tim Leathart, Bernhard Pfahringer, and Eibe Frank. Building ensembles of adaptive nested dichotomies with random-pair selection. In Proc 20th European Conference on Principles and Practice of Knowledge Discovery in Databases and 27th European Conference on Machine Learning, Riva del Garda, Italy, pages 179--194. Springer, 2016. [ bib | pdf ]
Felipe Bravo-Marquez, Eibe Frank, Saif M. Mohammad, and Bernhard Pfahringer. Determining word-emotion associations from tweets by multi-label classification. In Proc 15th IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, pages 536--539. IEEE Computer Society, 2016. [ bib | pdf ]
Felipe Bravo-Marquez, Eibe Frank, and Bernhard Pfahringer. From opinion lexicons to sentiment classification of tweets and vice versa: a transfer learning approach. In Proc 15th IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, pages 145--152. IEEE Computer Society, 2016. [ bib | pdf ]
Christopher Beckham, Mark Hall, and Eibe Frank. WekaPyScript: Classification, regression, and filter schemes for WEKA implemented in Python. Journal of Open Research Software, 4(1):e33, 2016. [ bib | http ]
Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA, 4 edition, 2016. [ bib | .html ]
Eibe Frank, Mark A. Hall, and Ian H. Witten. The WEKA workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016. [ bib | .pdf ]
Eibe Frank, Michael Mayo, and Stefan Kramer. Alternating model trees. In Proc 30th ACM Symposium on Applied Computing, Data Mining Track, pages 871--878. ACM Press, 2015. [ bib | pdf ]
Felipe Bravo-Marquez, Eibe Frank, and Bernhard Pfahringer. Positive, negative, or neutral: Learning an expanded opinion lexicon from emoticon-annotated tweets. In Proc 23rd International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, pages 1229--1235. IJCAI/AAAI, 2015. [ bib | pdf ]
Felipe Bravo-Marquez, Eibe Frank, and Bernhard Pfahringer. From unlabelled tweets to Twitter-specific opinion words. In Proc 38th Annual ACM SIGIR Conference, Santiago de Chile, Chile, pages 743--746. ACM Press, 2015. [ bib | pdf ]
Markus Michael Rau, Stella Seitz, Fabrice Brimioulle, Eibe Frank, Oliver Friedrich, Daniel Gruen, and Ben Hoyle. Accurate photometric redshift probability density estimation - method comparison and application. Monthly Notices of the Royal Astronomical Society, 452(4):3710--3725, 2015. [ 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 ]
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, 9(8):1131--1137, 2014. [ bib | http ]
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, 17(6):535--542, 2013. [ bib | http ]
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, 96:7043--7053, 2013. [ bib | pdf | http ]
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 ]
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, pages 191--200. IEEE, 2013. [ 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 ]
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, 63(8):1593--1608, 2012. [ bib | pdf ]
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 ]
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, 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 | pdf | http ]
Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. Classifier chains for multi-label classification. Machine Learning, 85(3):333--359, 2011. [ bib | pdf | http ]
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 ]
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 ]
James Foulds and Eibe Frank. A review of multi-instance learning assumptions. Knowledge Engineering Review, 25(1):1--25, 2010. [ bib | pdf ]
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 ]
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, Atlanta, Georgia, pages 1268--1273. AAAI Press, 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 ]
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 ]
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, 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, Japan, pages 225--240. JMLR, 2010. [ bib | pdf ]
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 ]
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 ]
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, Nashville, Tennessee, pages 332--339. IEEE, 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 ]
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, Thessaloniki, Greece, pages 278--290. 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, Singapore, pages 1318--1327. ACL, 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 ]
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 ]
Eibe Frank and Remco Bouckaert. Conditional density estimation with class probability estimators. In Proc 1st Asian Conference on Machine Learning, Nanjing, China, pages 65--81. Springer, 2009. [ 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 ]
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 ]
Kathryn Hempstalk and Eibe Frank. Discriminating against new classes: One-class versus multi-class classification. In Proc 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, pages 325--336. Springer, 2008. [ bib | pdf ]
Eibe Frank and Mark Hall. Additive regression applied to a large-scale collaborative filtering problem. In Proc 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, pages 435--446. Springer, 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 ]
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 ]
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 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 | pdf | .ps.gz ]
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 ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 ]
Niels Landwehr, Mark Hall, and Eibe Frank. Logistic model trees. Machine Learning, 59(1-2):161--205, 2005. [ bib | pdf | .ps.gz ]
Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco, 2 edition, 2005. [ bib | .html ]
Eibe Frank and Gordon W. Paynter. Predicting library of congress classifications from library of congress subject headings. Journal of the Association for Information Science and Technology, 55(3):214--227, 2004. [ bib | 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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
Eibe Frank and Mark Hall. Visualizing class probability estimators. In Proc 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, pages 168--179. Springer, 2003. [ bib | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
Eibe Frank, Geoffrey Holmes, Richard Kirkby, and Mark Hall. Racing committees for large datasets. In Proc 5th International Conference on Discovery Science, Lübeck, Germany, pages 153--164. Springer, 2002. [ bib | pdf | .ps.gz ]
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 ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 bug in the description of the algorithm. Please consult [124] instead. [ bib ]
Eibe Frank and Mark Hall. A simple approach to ordinal classification. Technical Report 01/05, Department of Computer Science, University of Waikato, 2001. [ bib | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
Eibe Frank. Pruning Decision Trees and Lists. PhD thesis, Department of Computer Science, University of Waikato, 2000. [ bib | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 [129]. [ bib | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, 2000. [ bib | .html ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 ]
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 | pdf | .ps.gz ]
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 ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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 | pdf | .ps.gz ]
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