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ORCIDEibe Frank
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- affiliation: University of Waikato, Hamilton, New Zealand
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Books and Theses
- 2011
[b4]Ian H. Witten, Eibe Frank, Mark A. Hall:
Data mining: practical machine learning tools and techniques, 3rd Edition. Morgan Kaufmann, Elsevier 2011, ISBN 9780123748560, pp. I-XXXIII, 1-629- 2008
- [b3]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 - 2005
- [b2]Ian H. Witten, Eibe Frank:
Data mining - practical machine learning tools and techniques, Second Edition. The Morgan Kaufmann series in data management systems, Morgan Kaufmann 2005, ISBN 978-0-12-088407-0, pp. I-XXXI, 1-525 - 1999
- [b1]Ian H. Witten, Eibe Frank:
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann 1999, ISBN 1-55860-552-5
Journal Articles
- 2024
- [j36]Julia R. Falconer
, Eibe Frank, Devon L. L. Polaschek, Chaitanya Joshi:
Eliciting Informative Priors by Modeling Expert Decision Making. Decis. Anal. 21(2): 77-90 (2024) - [j35]Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoff Holmes:
Feature extractor stacking for cross-domain few-shot learning. Mach. Learn. 113(1): 121-158 (2024) - [j34]Zac Pullar-Strecker, Katharina Dost, Eibe Frank, Jörg Wicker:
Hitting the target: stopping active learning at the cost-based optimum. Mach. Learn. 113(4): 1529-1547 (2024) - 2023
- [j33]Mi Li, Eibe Frank, Bernhard Pfahringer:
Large scale K-means clustering using GPUs. Data Min. Knowl. Discov. 37(1): 67-109 (2023) - [j32]Jesus Antonanzas, Yunzhe Jia, Eibe Frank, Albert Bifet, Bernhard Pfahringer:
teex: A toolbox for the evaluation of explanations. Neurocomputing 555: 126642 (2023) - 2022
- [j31]Julia R. Falconer, Eibe Frank, Devon L. L. Polaschek, Chaitanya Joshi:
Methods for Eliciting Informative Prior Distributions: A Critical Review. Decis. Anal. 19(3): 189-204 (2022) - [j30]Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes:
Sampling Permutations for Shapley Value Estimation. J. Mach. Learn. Res. 23: 43:1-43:46 (2022) - [j29]Rory Mitchell, Eibe Frank, Geoff Holmes:
GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles. PeerJ Comput. Sci. 8: e880 (2022) - [j28]Rory Mitchell, Daniel Stokes, Eibe Frank, Geoffrey Holmes:
Bandwidth-Optimal Random Shuffling for GPUs. ACM Trans. Parallel Comput. 9(1): 3:1-3:20 (2022) - 2021
- [j27]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 B. Ascher, Anping Song, Hiroki Kajita, David Gillespie, Neil D. Reeves, Joseph M. Pappachan, Claire O'Shea, Eibe Frank:
Deep learning in diabetic foot ulcers detection: A comprehensive evaluation. Comput. Biol. Medicine 135: 104596 (2021) - [j26]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier Chains: A Review and Perspectives. J. Artif. Intell. Res. 70: 683-718 (2021) - [j25]Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree:
Regularisation of neural networks by enforcing Lipschitz continuity. Mach. Learn. 110(2): 393-416 (2021) - [j24]Rory Mitchell, Eibe Frank, Geoffrey Holmes:
An Empirical Study of Moment Estimators for Quantile Approximation. ACM Trans. Database Syst. 46(1): 3:1-3:21 (2021) - 2020
- [j23]Michael Mayo, Eibe Frank:
Improving Naive Bayes for Regression with Optimized Artificial Surrogate Data. Appl. Artif. Intell. 34(6): 484-514 (2020) - 2019
- [j22]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad:
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets. J. Mach. Learn. Res. 20: 92:1-92:6 (2019) - [j21]Steven Lang, Felipe Bravo-Marquez, Christopher Beckham, Mark A. Hall, Eibe Frank:
WekaDeeplearning4j: A deep learning package for Weka based on Deeplearning4j. Knowl. Based Syst. 178: 48-50 (2019) - 2018
- [j20]Michael Geilke, Andreas Karwath, Eibe Frank, Stefan Kramer:
Online estimation of discrete, continuous, and conditional joint densities using classifier chains. Data Min. Knowl. Discov. 32(3): 561-603 (2018) - [j19]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Transferring sentiment knowledge between words and tweets. Web Intell. 16(4): 203-220 (2018) - 2017
- [j18]Rory Mitchell, Eibe Frank:
Accelerating the XGBoost algorithm using GPU computing. PeerJ Comput. Sci. 3: e127 (2017) - 2016
- [j17]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Building a Twitter opinion lexicon from automatically-annotated tweets. Knowl. Based Syst. 108: 65-78 (2016) - 2012
- [j16]Anna-Lan Huang, David N. Milne, Eibe Frank, Ian H. Witten:
Learning a concept-based document similarity measure. J. Assoc. Inf. Sci. Technol. 63(8): 1593-1608 (2012) - [j15]Albert Bifet, Eibe Frank, Geoff Holmes, Bernhard Pfahringer:
Ensembles of Restricted Hoeffding Trees. ACM Trans. Intell. Syst. Technol. 3(2): 30:1-30:20 (2012) - 2011
- [j14]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier chains for multi-label classification. Mach. Learn. 85(3): 333-359 (2011) - 2010
- [j13]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. J. Mach. Learn. Res. 11: 2533-2541 (2010) - [j12]James R. Foulds, Eibe Frank:
A review of multi-instance learning assumptions. Knowl. Eng. Rev. 25(1): 1-25 (2010) - [j11]Arthur Zimek, Fabian Buchwald, Eibe Frank, Stefan Kramer:
A Study of Hierarchical and Flat Classification of Proteins. IEEE ACM Trans. Comput. Biol. Bioinform. 7(3): 563-571 (2010) - 2009
- [j10]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) - [j9]Mark A. Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten:
The WEKA data mining software: an update. SIGKDD Explor. 11(1): 10-18 (2009) - 2005
- [j8]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. Comput. Biol. Chem. 29(1): 37-46 (2005) - [j7]Niels Landwehr, Mark A. Hall, Eibe Frank:
Logistic Model Trees. Mach. Learn. 59(1-2): 161-205 (2005) - 2004
- [j6]Eibe Frank, Mark A. Hall, Leonard E. Trigg, Geoffrey Holmes, Ian H. Witten:
Data mining in bioinformatics using Weka. Bioinform. 20(15): 2479-2481 (2004) - [j5]Eibe Frank, Gordon W. Paynter:
Predicting Library of Congress classifications from Library of Congress subject headings. J. Assoc. Inf. Sci. Technol. 55(3): 214-227 (2004) - 2001
- [j4]Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark A. Hall, Ian H. Witten:
Interactive machine learning: letting users build classifiers. Int. J. Hum. Comput. Stud. 55(3): 281-292 (2001) - 2000
- [j3]Eibe Frank, Leonard E. Trigg, Geoffrey Holmes, Ian H. Witten:
Naive Bayes for Regression (Technical Note). Mach. Learn. 41(1): 5-25 (2000) - 1999
- [j2]Carl Gutwin, Gordon W. Paynter, Ian H. Witten, Craig G. Nevill-Manning, Eibe Frank:
Improving browsing in digital libraries with keyphrase indexes. Decis. Support Syst. 27(1-2): 81-104 (1999) - 1998
- [j1]Eibe Frank, Yong Wang, Stuart Inglis, Geoffrey Holmes, Ian H. Witten:
Using Model Trees for Classification. Mach. Learn. 32(1): 63-76 (1998)
Conference and Workshop Papers
- 2023
- [c80]Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Self-trained Centroid Classifiers for Semi-supervised Cross-domain Few-shot Learning. CoLLAs 2023: 481-492 - [c79]Geoffrey Holmes, Eibe Frank, Dale Fletcher:
Image Classification Using Class-Agnostic Object Detection. AIAI (1) 2023: 255-266 - 2022
- [c78]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Better Self-training for Image Classification Through Self-supervision. AI 2022: 645-657 - [c77]Hongyu Wang, Huon Fraser, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoff Holmes:
Experiments in Cross-domain Few-shot Learning for Image Classification: Extended Abstract. Meta-Knowledge Transfer @ ECML/PKDD 2022: 81-83 - [c76]Geoff Holmes, Eibe Frank, Dale Fletcher, Corey Sterling:
Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models. IUI 2022: 584-593 - [c75]Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, Yunzhe Jia:
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal. NeurIPS 2022 - 2021
- [c74]Daniel Bull, Nick Jin Sean Lim, Eibe Frank:
Perceptual improvements for Super-Resolution of Satellite Imagery. IVCNZ 2021: 1-6 - [c73]Yunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet, Nick Jin Sean Lim:
Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality. ECML/PKDD (2) 2021: 699-714 - 2020
- [c72]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Transfer of Pretrained Model Weights Substantially Improves Semi-supervised Image Classification. Australasian Conference on Artificial Intelligence 2020: 433-444 - [c71]Hongyu Wang, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo:
A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning. Australasian Conference on Artificial Intelligence 2020: 445-457 - [c70]Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, Albert Bifet:
Adaptive XGBoost for Evolving Data Streams. IJCNN 2020: 1-8 - [c69]Rhys Compton, Eibe Frank, Panos Patros, Abigail M. Y. Koay:
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation. MSR 2020: 243-253 - 2019
- [c68]Henry Gouk, Bernhard Pfahringer, Eibe Frank:
Stochastic Gradient Trees. ACML 2019: 1094-1109 - [c67]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Semi-supervised Learning Using Siamese Networks. Australasian Conference on Artificial Intelligence 2019: 586-597 - [c66]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
On Calibration of Nested Dichotomies. PAKDD (1) 2019: 69-80 - [c65]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Ensembles of Nested Dichotomies with Multiple Subset Evaluation. PAKDD (1) 2019: 81-93 - 2018
- [c64]Varvara Vetrova, Sheldon Coup, Eibe Frank, Michael J. Cree:
Hidden Features: Experiments with Feature Transfer for Fine-Grained Multi-Class and One-Class Image Categorization. IVCNZ 2018: 1-6 - [c63]Henry Gouk, Bernhard Pfahringer, Eibe Frank, Michael J. Cree:
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes. ECML/PKDD (1) 2018: 541-556 - [c62]Iakovos Gurulian, Konstantinos Markantonakis, Eibe Frank, Raja Naeem Akram:
Good Vibrations: Artificial Ambience-Based Relay Attack Detection. TrustCom/BigDataSE 2018: 481-489 - 2017
- [c61]Tim Leathart, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Probability Calibration Trees. ACML 2017: 145-160 - [c60]Jeff Mo, Eibe Frank, Varvara Vetrova:
Large-Scale Automatic Species Identification. Australasian Conference on Artificial Intelligence 2017: 301-312 - [c59]Paula Branco, Luís Torgo, Rita P. Ribeiro, Eibe Frank, Bernhard Pfahringer, Markus Michael Rau:
Learning Through Utility Optimization in Regression Tasks. DSAA 2017: 30-39 - [c58]Iakovos Gurulian, Konstantinos Markantonakis, Carlton Shepherd, Eibe Frank, Raja Naeem Akram:
Proximity Assurances Based on Natural and Artificial Ambient Environments. SECITC 2017: 83-103 - [c57]Carlton Shepherd, Iakovos Gurulian, Eibe Frank, Konstantinos Markantonakis, Raja Naeem Akram, Emmanouil Panaousis, Keith Mayes:
The Applicability of Ambient Sensors as Proximity Evidence for NFC Transactions. IEEE Symposium on Security and Privacy Workshops 2017: 179-188 - [c56]Iakovos Gurulian, Carlton Shepherd, Eibe Frank, Konstantinos Markantonakis, Raja Naeem Akram, Keith Mayes:
On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks. TrustCom/BigDataSE/ICESS 2017: 41-49 - 2016
- [c55]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis. ECAI 2016: 498-506 - [c54]Tim Leathart, Bernhard Pfahringer, Eibe Frank:
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection. ECML/PKDD (2) 2016: 179-194 - [c53]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach. WI 2016: 145-152 - [c52]Felipe Bravo-Marquez, Eibe Frank, Saif M. Mohammad, Bernhard Pfahringer:
Determining Word-Emotion Associations from Tweets by Multi-label Classification. WI 2016: 536-539 - 2015
- [c51]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets. IJCAI 2015: 1229-1235 - [c50]Eibe Frank, Michael Mayo, Stefan Kramer:
Alternating model trees. SAC 2015: 871-878 - [c49]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
From Unlabelled Tweets to Twitter-specific Opinion Words. SIGIR 2015: 743-746 - 2013
- [c48]Eibe Frank, Bernhard Pfahringer:
Propositionalisation of Multi-instance Data Using Random Forests. Australasian Conference on Artificial Intelligence 2013: 362-373 - [c47]Michael Geilke, Eibe Frank, Andreas Karwath, Stefan Kramer:
Online Estimation of Discrete Densities. ICDM 2013: 191-200 - 2011
- [c46]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 - 2010
- [c45]Fabian Buchwald, Tobias Girschick, Eibe Frank, Stefan Kramer:
Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships. AAAI 2010: 1268-1273 - [c44]Albert Bifet, Eibe Frank:
Sentiment Knowledge Discovery in Twitter Streaming Data. Discovery Science 2010: 1-15 - [c43]James R. Foulds, Eibe Frank:
Speeding Up and Boosting Diverse Density Learning. Discovery Science 2010: 102-116 - [c42]Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Eibe Frank:
Fast Perceptron Decision Tree Learning from Evolving Data Streams. PAKDD (2) 2010: 299-310 - [c41]Albert Bifet, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking. ACML 2010: 225-240 - 2009
- [c40]Eibe Frank, Remco R. Bouckaert:
Conditional Density Estimation with Class Probability Estimators. ACML 2009: 65-81 - [c39]Martin Gütlein, Eibe Frank, Mark A. Hall, Andreas Karwath:
Large-scale attribute selection using wrappers. CIDM 2009: 332-339 - [c38]Olena Medelyan, Eibe Frank, Ian H. Witten:
Human-competitive tagging using automatic keyphrase extraction. EMNLP 2009: 1318-1327 - [c37]Geoffrey Holmes, Dale Fletcher, Peter Reutemann, Eibe Frank:
Analysing chromatographic data using data mining to monitor petroleum content in water. ITEE 2009: 278-290 - [c36]Anna-Lan Huang, David N. Milne, Eibe Frank, Ian H. Witten:
Clustering Documents Using a Wikipedia-Based Concept Representation. PAKDD 2009: 628-636 - [c35]Jesse Read, Bernhard Pfahringer, Geoffrey Holmes, Eibe Frank:
Classifier Chains for Multi-label Classification. ECML/PKDD (2) 2009: 254-269 - 2008
- [c34]James R. Foulds, Eibe Frank:
Revisiting Multiple-Instance Learning Via Embedded Instance Selection. Australasian Conference on Artificial Intelligence 2008: 300-310 - [c33]Kathryn Hempstalk, Eibe Frank:
Discriminating Against New Classes: One-class versus Multi-class Classification. Australasian Conference on Artificial Intelligence 2008: 325-336 - [c32]Eibe Frank, Mark A. Hall:
Additive Regression Applied to a Large-Scale Collaborative Filtering Problem. Australasian Conference on Artificial Intelligence 2008: 435-446 - [c31]Mark A. Hall, Eibe Frank:
Combining Naive Bayes and Decision Tables. FLAIRS 2008: 318-319 - [c30]Anna-Lan Huang, David N. Milne, Eibe Frank, Ian H. Witten:
Clustering Documents with Active Learning Using Wikipedia. ICDM 2008: 839-844 - [c29]Kathryn Hempstalk, Eibe Frank, Ian H. Witten:
One-Class Classification by Combining Density and Class Probability Estimation. ECML/PKDD (1) 2008: 505-519 - 2007
- [c28]Ashraf M. Kibriya, Eibe Frank:
An Empirical Comparison of Exact Nearest Neighbour Algorithms. PKDD 2007: 140-151 - 2006
- [c27]Eibe Frank, Bernhard Pfahringer:
Improving on Bagging with Input Smearing. PAKDD 2006: 97-106 - [c26]Eibe Frank, Remco R. Bouckaert:
Naive Bayes for Text Classification with Unbalanced Classes. PKDD 2006: 503-510 - 2005
- [c25]Lin Dong, Eibe Frank, Stefan Kramer:
Ensembles of Balanced Nested Dichotomies for Multi-class Problems. PKDD 2005: 84-95 - [c24]Gabi Schmidberger, Eibe Frank:
Unsupervised Discretization Using Tree-Based Density Estimation. PKDD 2005: 240-251 - [c23]Marc Sumner, Eibe Frank, Mark A. Hall:
Speeding Up Logistic Model Tree Induction. PKDD 2005: 675-683 - 2004
- [c22]Ashraf M. Kibriya, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Multinomial Naive Bayes for Text Categorization Revisited. Australian Conference on Artificial Intelligence 2004: 488-499 - [c21]Stefan Mutter, Mark A. Hall, Eibe Frank:
Using Classification to Evaluate the Output of Confidence-Based Association Rule Mining. Australian Conference on Artificial Intelligence 2004: 538-549 - [c20]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 - [c19]Eibe Frank, Stefan Kramer:
Ensembles of nested dichotomies for multi-class problems. ICML 2004 - [c18]Remco R. Bouckaert, Eibe Frank:
Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. PAKDD 2004: 3-12 - [c17]Xin Xu, Eibe Frank:
Logistic Regression and Boosting for Labeled Bags of Instances. PAKDD 2004: 272-281 - 2003
- [c16]Niels Landwehr, Mark A. Hall, Eibe Frank:
Logistic Model Trees. ECML 2003: 241-252 - [c15]Nils Weidmann, Eibe Frank, Bernhard Pfahringer:
A Two-Level Learning Method for Generalized Multi-instance Problems. ECML 2003: 468-479 - [c14]Eibe Frank, Mark A. Hall:
Visualizing Class Probability Estimators. PKDD 2003: 168-179 - [c13]Eibe Frank, Mark A. Hall, Bernhard Pfahringer:
Locally Weighted Naive Bayes. UAI 2003: 249-256 - 2002
- [c12]Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark A. Hall:
Racing Committees for Large Datasets. Discovery Science 2002: 153-164 - [c11]Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark A. Hall:
Multiclass Alternating Decision Trees. ECML 2002: 161-172 - 2001
- [c10]Eibe Frank, Mark A. Hall:
A Simple Approach to Ordinal Classification. ECML 2001: 145-156 - [c9]Andrew Turpin, Eibe Frank, Mark A. Hall, Ian H. Witten, Chris A. Johnson:
Determining Progression in Glaucoma Using Visual Fields. PAKDD 2001: 136-147 - 2000
- [c8]Eibe Frank, Chang Chui, Ian H. Witten:
Text Categorization Using Compression Models. Data Compression Conference 2000: 555 - [c7]Stefan Kramer, Eibe Frank:
Bottom-Up Propositionalization. ILP Work-in-progress reports 2000 - 1999
- [c6]Geoffrey Holmes, Mark A. Hall, Eibe Frank:
Generating Rule Sets from Model Trees. Australian Joint Conference on Artificial Intelligence 1999: 1-12 - [c5]Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin, Craig G. Nevill-Manning:
KEA: Practical Automatic Keyphrase Extraction. ACM DL 1999: 254-255 - [c4]Eibe Frank, Ian H. Witten:
Making Better Use of Global Discretization. ICML 1999: 115-123 - [c3]Eibe Frank, Gordon W. Paynter, Ian H. Witten, Carl Gutwin, Craig G. Nevill-Manning:
Domain-Specific Keyphrase Extraction. IJCAI 1999: 668-673 - 1998
- [c2]Eibe Frank, Ian H. Witten:
Generating Accurate Rule Sets Without Global Optimization. ICML 1998: 144-151 - [c1]Eibe Frank, Ian H. Witten:
Using a Permutation Test for Attribute Selection in Decision Trees. ICML 1998: 152-160
Parts in Books or Collections
- 2016
- [p3]Tony C. Smith, Eibe Frank:
Introducing Machine Learning Concepts with WEKA. Statistical Genomics 2016: 353-378 - 2010
- [p2]Eibe Frank, Mark A. 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 - 2005
- [p1]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
Informal and Other Publications
- 2024
- [i27]Xin Xu, Eibe Frank, Geoffrey Holmes:
Multiple Instance Verification. CoRR abs/2407.06544 (2024) - 2022
- [i26]Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoffrey Holmes:
Cross-domain Few-shot Meta-learning Using Stacking. CoRR abs/2205.05831 (2022) - [i25]Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, Yunzhe Jia:
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal. CoRR abs/2209.13917 (2022) - 2021
- [i24]Rory Mitchell, Joshua Cooper, Eibe Frank, Geoffrey Holmes:
Sampling Permutations for Shapley Value Estimation. CoRR abs/2104.12199 (2021) - [i23]Rory Mitchell, Daniel Stokes, Eibe Frank, Geoffrey Holmes:
Bandwidth-Optimal Random Shuffling for GPUs. CoRR abs/2106.06161 (2021) - [i22]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Better Self-training for Image Classification through Self-supervision. CoRR abs/2109.00778 (2021) - [i21]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification. CoRR abs/2109.00788 (2021) - [i20]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Semi-Supervised Learning using Siamese Networks. CoRR abs/2109.00794 (2021) - [i19]Zac Pullar-Strecker, Katharina Dost, Eibe Frank, Jörg Wicker:
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum. CoRR abs/2110.03802 (2021) - 2020
- [i18]Rhys Compton, Eibe Frank, Panos Patros, Abigail M. Y. Koay:
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation. CoRR abs/2004.02942 (2020) - [i17]Bill Cassidy, Neil D. Reeves, Joseph Pappachan, David Gillespie, Claire O'Shea, Satyan Rajbhandari, Arun G. Maiya, Eibe Frank, Andrew Boulton, David G. Armstrong, Bijan Najafi, Justina Wu, Moi Hoon Yap:
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection. CoRR abs/2004.11853 (2020) - [i16]Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, Albert Bifet:
Adaptive XGBoost for Evolving Data Streams. CoRR abs/2005.07353 (2020) - [i15]Moi Hoon Yap, Ryo Hachiuma, Azadeh Alavi, Raphael Brüngel, Manu Goyal, Hongtao Zhu, Bill Cassidy, Johannes Rückert, Moshe Olshansky, Xiao Huang, Hideo Saito, Saeed Hassanpour, Christoph M. Friedrich, David B. Ascher, Anping Song, Hiroki Kajita, David Gillespie, Neil D. Reeves, Joseph Pappachan, Claire O'Shea, Eibe Frank:
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation. CoRR abs/2010.03341 (2020) - [i14]Rory Mitchell, Eibe Frank, Geoffrey Holmes:
GPUTreeShap: Fast Parallel Tree Interpretability. CoRR abs/2010.13972 (2020) - 2019
- [i13]Henry Gouk, Bernhard Pfahringer, Eibe Frank:
Stochastic Gradient Trees. CoRR abs/1901.07777 (2019) - [i12]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier Chains: A Review and Perspectives. CoRR abs/1912.13405 (2019) - 2018
- [i11]Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree:
Regularisation of Neural Networks by Enforcing Lipschitz Continuity. CoRR abs/1804.04368 (2018) - [i10]Henry Gouk, Bernhard Pfahringer, Eibe Frank, Michael J. Cree:
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes. CoRR abs/1804.05965 (2018) - [i9]Rory Mitchell, Andrey Adinets, Thejaswi Rao, Eibe Frank:
XGBoost: Scalable GPU Accelerated Learning. CoRR abs/1806.11248 (2018) - [i8]Tim Leathart, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Probability Calibration Trees. CoRR abs/1808.00111 (2018) - [i7]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Ensembles of Nested Dichotomies with Multiple Subset Evaluation. CoRR abs/1809.02740 (2018) - [i6]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
On the Calibration of Nested Dichotomies for Large Multiclass Tasks. CoRR abs/1809.02744 (2018) - 2017
- [i5]Michael Mayo, Eibe Frank:
Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data. CoRR abs/1707.04943 (2017) - [i4]Rory Mitchell, Eibe Frank:
Accelerating the XGBoost algorithm using GPU computing. PeerJ Prepr. 5: e2911 (2017) - 2016
- [i3]Tim Leathart, Bernhard Pfahringer, Eibe Frank:
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection. CoRR abs/1604.01854 (2016) - 2012
- [i2]Eibe Frank, Mark A. Hall, Bernhard Pfahringer:
Locally Weighted Naive Bayes. CoRR abs/1212.2487 (2012) - 1999
- [i1]Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl Gutwin, Craig G. Nevill-Manning:
KEA: Practical Automatic Keyphrase Extraction. CoRR cs.DL/9902007 (1999)
Coauthor Index
[j35] [j33] [j32] [j26] [j25] [j22] [j19] [j17] [j15] [j14] [j13] [j9] [c80] [c78] [c77] [c75] [c73] [c72] [c71] [c70] [c68] [c67] [c66] [c65] [c63] [c61] [c59] [c55] [c54] [c53] [c52] [c51] [c49] [c48] [c42] [c41] [c35] [c27] [c22] [c20] [c15] [c13] [c11] [p2] [p1] [i26] [i25] [i22] [i21] [i20] [i16] [i13] [i12] [i11] [i10] [i8] [i7] [i6] [i3] [i2]
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