Eibe Frank

Anna-Lan Huang, David N. Milne, Eibe Frank, Ian H. Witten: Learning a concept-based document similarity measure. JASIST 63(8): 1593-1608 (2012)

Albert Bifet, Eibe Frank, Geoff Holmes, Bernhard Pfahringer: Ensembles of Restricted Hoeffding Trees. ACM TIST 3(2): 30 (2012)

Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. CoRR abs/1212.2487 (2012)

Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank: Classifier chains for multi-label classification. Machine Learning 85(3): 333-359 (2011)

Luke Bjerring, Eibe Frank: Beyond Trees: Adopting MITI to Learn Rules and Ensemble Classifiers for Multi-Instance Data. Australasian Conference on Artificial Intelligence 2011: 41-50

Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten: WEKA - Experiences with a Java Open-Source Project. Journal of Machine Learning Research 11: 2533-2541 (2010)

Albert Bifet, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer: Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking. Journal of Machine Learning Research - Proceedings Track 13: 225-240 (2010)

James R. Foulds, Eibe Frank: A review of multi-instance learning assumptions. Knowledge Eng. Review 25(1): 1-25 (2010)

Arthur Zimek, Fabian Buchwald, Eibe Frank, Stefan Kramer: A Study of Hierarchical and Flat Classification of Proteins. IEEE/ACM Trans. Comput. Biology Bioinform. 7(3): 563-571 (2010)

Fabian Buchwald, Tobias Girschick, Eibe Frank, Stefan Kramer: Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships. AAAI 2010

Albert Bifet, Eibe Frank: Sentiment Knowledge Discovery in Twitter Streaming Data. Discovery Science 2010: 1-15

James R. Foulds, Eibe Frank: Speeding Up and Boosting Diverse Density Learning. Discovery Science 2010: 102-116

Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Eibe Frank: Fast Perceptron Decision Tree Learning from Evolving Data Streams. PAKDD (2) 2010: 299-310

Eibe Frank, Mark Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer, Ian H. Witten, Len Trigg: Weka-A Machine Learning Workbench for Data Mining. Data Mining and Knowledge Discovery Handbook 2010: 1269-1277

Arie Ben-David, Eibe Frank: Accuracy of machine learning models versus "hand crafted" expert systems - A credit scoring case study. Expert Syst. Appl. 36(3): 5264-5271 (2009)

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

Eibe Frank, Remco R. Bouckaert: Conditional Density Estimation with Class Probability Estimators. ACML 2009: 65-81

Martin Gütlein, Eibe Frank, Mark Hall, Andreas Karwath: Large-scale attribute selection using wrappers. CIDM 2009: 332-339

Olena Medelyan, Eibe Frank, Ian H. Witten: Human-competitive tagging using automatic keyphrase extraction. EMNLP 2009: 1318-1327

Geoffrey Holmes, Dale Fletcher, Peter Reutemann, Eibe Frank: Analysing chromatographic data using data mining to monitor petroleum content in water. ITEE 2009: 278-290

Anna-Lan Huang, David N. Milne, Eibe Frank, Ian H. Witten: Clustering Documents Using a Wikipedia-Based Concept Representation. PAKDD 2009: 628-636

Jesse Read, Bernhard Pfahringer, Geoffrey Holmes, Eibe Frank: Classifier Chains for Multi-label Classification. ECML/PKDD (2) 2009: 254-269

Soumen Chakrabarti, Earl Cox, Eibe Frank, Ralf Hartmut Güting, Jiawei Han, Xia Jiang, Micheline Kamber, Sam Lightstone, Thomas P. Nadeau, Richard E. Neapolitan, Dorian Pyle, Mamdouh Refaat, Markus Schneider, Toby J. Teorey, Ian H. Witten: Data Mining - Know It All. Morgan Kaufmann 2008, isbn 978-0-12-374629-0, pp. I-XIII, 1-460

James R. Foulds, Eibe Frank: Revisiting Multiple-Instance Learning Via Embedded Instance Selection. Australasian Conference on Artificial Intelligence 2008: 300-310

Kathryn Hempstalk, Eibe Frank: Discriminating Against New Classes: One-class versus Multi-class Classification. Australasian Conference on Artificial Intelligence 2008: 325-336

Eibe Frank, Mark Hall: Additive Regression Applied to a Large-Scale Collaborative Filtering Problem. Australasian Conference on Artificial Intelligence 2008: 435-446

Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. FLAIRS Conference 2008: 318-319

Anna-Lan Huang, David N. Milne, Eibe Frank, Ian H. Witten: Clustering Documents with Active Learning Using Wikipedia. ICDM 2008: 839-844

Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. ECML/PKDD (1) 2008: 505-519

Ashraf M. Kibriya, Eibe Frank: An Empirical Comparison of Exact Nearest Neighbour Algorithms. PKDD 2007: 140-151

Eibe Frank, Bernhard Pfahringer: Improving on Bagging with Input Smearing. PAKDD 2006: 97-106

Eibe Frank, Remco R. Bouckaert: Naive Bayes for Text Classification with Unbalanced Classes. PKDD 2006: 503-510

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

Niels Landwehr, Mark Hall, Eibe Frank: Logistic Model Trees. Machine Learning 59(1-2): 161-205 (2005)

Eibe Frank, Mark A. Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer: WEKA - A Machine Learning Workbench for Data Mining. The Data Mining and Knowledge Discovery Handbook 2005: 1305-1314

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. PKDD 2005: 84-95

Gabi Schmidberger, Eibe Frank: Unsupervised Discretization Using Tree-Based Density Estimation. PKDD 2005: 240-251

Marc Sumner, Eibe Frank, Mark A. Hall: Speeding Up Logistic Model Tree Induction. PKDD 2005: 675-683

Eibe Frank, Mark Hall, Leonard E. Trigg, Geoffrey Holmes, Ian H. Witten: Data mining in bioinformatics using Weka. Bioinformatics 20(15): 2479-2481 (2004)

Eibe Frank, Gordon W. Paynter: Predicting Library of Congress classifications from Library of Congress subject headings. JASIST 55(3): 214-227 (2004)

Ashraf M. Kibriya, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes: Multinomial Naive Bayes for Text Categorization Revisited. Australian Conference on Artificial Intelligence 2004: 488-499

Stefan Mutter, Mark Hall, Eibe Frank: Using Classification to Evaluate the Output of Confidence-Based Association Rule Mining. Australian Conference on Artificial Intelligence 2004: 538-549

Peter Reutemann, Bernhard Pfahringer, Eibe Frank: A Toolbox for Learning from Relational Data with Propositional and Multi-instance Learners. Australian Conference on Artificial Intelligence 2004: 1017-1023

Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. ICML 2004

Remco R. Bouckaert, Eibe Frank: Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. PAKDD 2004: 3-12

Xin Xu, Eibe Frank: Logistic Regression and Boosting for Labeled Bags of Instances. PAKDD 2004: 272-281

Niels Landwehr, Mark Hall, Eibe Frank: Logistic Model Trees. ECML 2003: 241-252

Nils Weidmann, Eibe Frank, Bernhard Pfahringer: A Two-Level Learning Method for Generalized Multi-instance Problems. ECML 2003: 468-479

Eibe Frank, Mark Hall: Visualizing Class Probability Estimators. PKDD 2003: 168-179

Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. UAI 2003: 249-256

Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing Committees for Large Datasets. Discovery Science 2002: 153-164

Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass Alternating Decision Trees. ECML 2002: 161-172

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

Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. ECML 2001: 145-156

Andrew Turpin, Eibe Frank, Mark Hall, Ian H. Witten, Chris A. Johnson: Determining Progression in Glaucoma Using Visual Fields. PAKDD 2001: 136-147

Eibe Frank, Leonard E. Trigg, Geoffrey Holmes, Ian H. Witten: Naive Bayes for Regression (Technical Note). Machine Learning 41(1): 5-25 (2000)

Eibe Frank, Chang Chui, Ian H. Witten: Text Categorization Using Compression Models. Data Compression Conference 2000: 555

Stefan Kramer, Eibe Frank: Bottom-Up Propositionalization. ILP Work-in-progress reports 2000

Ian H. Witten, Eibe Frank: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann 1999, isbn 1-55860-552-5

Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. Australian Joint Conference on Artificial Intelligence 1999: 1-12

Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin, Craig G. Nevill-Manning: KEA: Practical Automatic Keyphrase Extraction. ACM DL 1999: 254-255

Eibe Frank, Ian H. Witten: Making Better Use of Global Discretization. ICML 1999: 115-123

Eibe Frank, Gordon W. Paynter, Ian H. Witten, Carl Gutwin, Craig G. Nevill-Manning: Domain-Specific Keyphrase Extraction. IJCAI 1999: 668-673

Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin, Craig G. Nevill-Manning: KEA: Practical Automatic Keyphrase Extraction. CoRR cs.DL/9902007 (1999)

Eibe Frank, Yong Wang, Stuart Inglis, Geoffrey Holmes, Ian H. Witten: Using Model Trees for Classification. Machine Learning 32(1): 63-76 (1998)

Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. ICML 1998: 144-151

Eibe Frank, Ian H. Witten: Using a Permutation Test for Attribute Selection in Decision Trees. ICML 1998: 152-160