B. Pfahringer

发表

Regularisation of neural networks by enforcing Lipschitz continuity pdf

Bernhard Pfahringer, Michael J. Cree, Eibe Frank, 2018, Machine Learning.

The WEKA data mining software: an update

Ian H. Witten, Geoff Holmes, Bernhard Pfahringer, 2009, SKDD.

Addressing feature drift in data streams using iterative subset selection

B. Pfahringer, Lanqin Yuan, J. P. Barddal, 2019, SIAP.

Classifier chains for multi-label classification

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2009, Machine Learning.

Generating Synthetic Multi-label Data Streams

B. Pfahringer, G. Holmes, J. Read, 2009 .

Classifier Chains for Multi-label Classification

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2009, ECML/PKDD.

Meta-Learning by Landmarking Various Learning Algorithms

Hilan Bensusan, Bernhard Pfahringer, Christophe G. Giraud-Carrier, 2000, ICML.

Merit-guided dynamic feature selection filter for data streams

Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, 2019, Expert Syst. Appl..

Weka-A Machine Learning Workbench for Data Mining

Ian H. Witten, Bernhard Pfahringer, Richard Kirkby, 2005, Data Mining and Knowledge Discovery Handbook.

Multinomial Naive Bayes for Text Categorization Revisited

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2004, Australian Conference on Artificial Intelligence.

A semi-supervised Spam mail detector

Bernhard Pfahringer, B. Pfahringer, 2006 .

A new way to share, organize and learn from experiments

Hendrik Blockeel, Joaquin Vanschoren, Bernhard Pfahringer, 2012 .

Learning from the Past with Experiment Databases

Geoff Holmes, Joaquin Vanschoren, Bernhard Pfahringer, 2008, PRICAI.

The Futility of) Trying to Predict Carcinogenicity of Chemical Compounds

Bernhard Pfahringer, B. Pfahringer, 2001 .

Data Quality Issues in Toxicological Knowledge Discovery

Bernhard Pfahringer, Christoph Helma, Eva Gottmann, 1999 .

Ensembles of Nested Dichotomies with Multiple Subset Evaluation

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2018, PAKDD.

Hierarchical Meta-Rules for Scalable Meta-Learning

Quan Sun, Bernhard Pfahringer, B. Pfahringer, 2014, PRICAI.

Machine Learning (In) Security: A Stream of Problems pdf

Luiz Eduardo Soares de Oliveira, André Ricardo Abed Grégio, Heitor Murilo Gomes, 2020, Digital Threats: Research and Practice.

Compression-Based Discretization of Continuous Attributes

Bernhard Pfahringer, B. Pfahringer, 1995, ICML.

MACHINE LEARNING FOR DATA STREAMS

Albert Bifet, Ricard Gavaldà, Bernhard Pfahringer, 2018 .

Conjunctive Normal Form

Bernhard Pfahringer, B. Pfahringer, 2010, Encyclopedia of Machine Learning.

CD-MOA: Change Detection Framework for Massive Online Analysis

Geoff Holmes, Jesse Read, Albert Bifet, 2013, IDA.

Winning the KDD99 classification cup: bagged boosting

Bernhard Pfahringer, B. Pfahringer, 2000, SKDD.

Preface Ecml-pkdd Discovery Challenge 2006 Overview

B. Pfahringer, Asim Karim, Khurram Junejo, 2006 .

Practical Uses of the Minimum Description Length Principle in Inductive Learning

B. Pfahringer, 1995 .

High density-focused uncertainty sampling for active learning over evolving stream data

Dino Ienco, Bernhard Pfahringer, Indre Zliobaite, 2014, BigMine.

Comp420-05y Report Mining Gene Expression Data

B. Pfahringer, 2005 .

Propositionalization through stochastic discrimination

Bernhard Pfahringer, Geoffrey Holmes, B. Pfahringer, 2003 .

u EXPERIMENTS IN PREDICTING BIODEGRADABILITY

S. Džeroski, Stefan Kramer, B. Pfahringer, 2004 .

Click-Through Rate (CTR)

Jean-Michel Renders, João Gama, Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning and Data Mining.

{19 () Improving Bagging Performance by Increasing Decision Tree Diversity

B. Pfahringer, 1997 .

Classification Tree

John Case, Sanjay Jain, Jean-Michel Renders, 2010, Encyclopedia of Machine Learning.

On the Induction of Intelligible Ensembles

B. Pfahringer, 1997 .

LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2022, Inf. Sci..

Complex Adaptive System

John Case, Jean-Michel Renders, Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning.

Text Categorisation Using Do ument Pro ling

B. Pfahringer, 2007 .

Mining data streams using option trees

Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, 2003 .

Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing pdf

Heitor Murilo Gomes, Albert Bifet, Hermes Senger, 2022, IEEE Transactions on Network and Service Management.

Eﬀicient Batch-Incremental Classification Using UMAP for Evolving Data Streams

A. Bifet, B. Pfahringer, S. Maniu, 2022 .

SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2022, Data Mining and Knowledge Discovery.

CLP(gRel): Explicit Manipulation of (ground) Relational Dependencies in Logic Programming

B. Pfahringer, 2007 .

Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks

B. Pfahringer, Michael Mayo, Chen Zheng, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Streaming Data Mining with Massive Online Analytics (MOA)

Geoff Holmes, Jesse Read, Albert Bifet, 2018 .

Certainty Equivalence Principle

Jean-Michel Renders, João Gama, Lise Getoor, 2010, Encyclopedia of Machine Learning and Data Mining.

Scalable Multi-label Classification

Geoff Holmes, Bernhard Pfahringer, B. Pfahringer, 2010 .

A logic boosting approach to inducing multiclass alternating decision trees

Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, 2002 .

Millions of random rules

Cheng Wang, Geoffrey Holmes, Bernhard Pfahringer, 2004 .

Cross-domain Few-shot Meta-learning Using Stacking

B. Pfahringer, Eibe Frank, Michael Mayo, 2022, ArXiv.

Feature Extractor Stacking for Cross-domain Few-shot Meta-learning

B. Pfahringer, Eibe Frank, Michael Mayo, 2022, 2205.05831.

Disjunctive Normal Form

Bernhard Pfahringer, B. Pfahringer, 2017, Encyclopedia of Machine Learning and Data Mining.

The Logical Way to Build a DL-based KR System

Bernhard Pfahringer, B. Pfahringer, 1992, Description Logics.

VIE-DU: Dialogue by Unification

Bernhard Pfahringer, Ernst Buchberger, Johannes Matiasek, 1991, ÖGAI.

Bagging Ensemble Selection for Regression

Quan Sun, Bernhard Pfahringer, B. Pfahringer, 2012, Australasian Conference on Artificial Intelligence.

SMOTE for Regression

Luís Torgo, Rita P. Ribeiro, Bernhard Pfahringer, 2013, EPIA.

Random Relational Rules

Bernhard Pfahringer, Grant Anderson, B. Pfahringer, 2006 .

Text Categorisation Using Document Profiling

Bernhard Pfahringer, Maximilien Sauban, B. Pfahringer, 2003, PKDD.

A Two-Level Learning Method for Generalized Multi-instance Problems

Bernhard Pfahringer, Nils Weidmann, Eibe Frank, 2003, ECML.

Classifier Chains: A Review and Perspectives pdf

Bernhard Pfahringer, Jesse Read, Geoff Holmes, 2019, J. Artif. Intell. Res..

Multi-label Classification Using Ensembles of Pruned Sets

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2008, 2008 Eighth IEEE International Conference on Data Mining.

An effective evaluation measure for clustering on evolving data streams

Thomas Seidl, Geoff Holmes, Albert Bifet, 2011, KDD.

Clustering for classification

Geoffrey Holmes, Bernhard Pfahringer, Reuben Evans, 2011, 2011 7th International Conference on Information Technology in Asia.

Clustering for Classification Subtitle as neede

Bernhard Pfahringer, Geoffrey Holmes, Reuben Evans, 2011 .

Adaptive XGBoost for Evolving Data Streams

Albert Bifet, Talel Abdessalem, Jacob Montiel, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Leveraging Bagging for Evolving Data Streams

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, ECML/PKDD.

Stress-Testing Hoeffding Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2005, PKDD.

Data quality in predictive toxicology: identification of chemical structures and calculation of chemical properties.

E Gottmann, S Kramer, C Helma, 2000, Environmental health perspectives.

EXPERIMENTS IN PREDICTING BIODEGRADABILITY

Saso Dzeroski, Wim Van Laer, Hendrik Blockeel, 1999, ILP.

Scaling Up Semi-supervised Learning: An Efficient and Effective LLGC Variant

Claire Leschi, Bernhard Pfahringer, Peter Reutemann, 2007, PAKDD.

A discriminative approach to structured biological data

Bernhard Pfahringer, Stefan Mutter, B. Pfahringer, 2007 .

Multiclass Alternating Decision Trees

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2002, ECML.

Locally Weighted Naive Bayes pdf

Bernhard Pfahringer, Eibe Frank, Mark A. Hall, 2002, UAI.

Compression-Based Pruning of Decision Lists

Bernhard Pfahringer, B. Pfahringer, 1997, ECML.

The Positive Effects of Negative Information: Extending One-Class Classification Models in Binary Proteomic Sequence Classification

Geoff Holmes, Bernhard Pfahringer, Stefan Mutter, 2009, Australasian Conference on Artificial Intelligence.

The Weka solution to the 2004 KDD Cup

Bernhard Pfahringer, B. Pfahringer, 2004, SKDD.

Pairwise meta-rules for better meta-learning-based algorithm ranking

Quan Sun, Bernhard Pfahringer, B. Pfahringer, 2013, Machine Learning.

Resampling strategies for regression

Luís Torgo, Rita P. Ribeiro, Bernhard Pfahringer, 2015, Expert Syst. J. Knowl. Eng..

Random model trees: an effective and scalable regression method

Bernhard Pfahringer, B. Pfahringer, 2010 .

Clustering Large Datasets Using Cobweb and K-Means in Tandem

Geoff Holmes, Mi Li, Bernhard Pfahringer, 2004, Australian Conference on Artificial Intelligence.

C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams

Emanuele Della Valle, Heitor Murilo Gomes, A. Bifet, 2020, 2020 IEEE International Conference on Big Data (Big Data).

Data quality in predictive toxicology: reproducibility of rodent carcinogenicity experiments.

C Helma, E Gottmann, B Pfahringer, 2001, Environmental health perspectives.

Stochastic Gradient Trees pdf

Bernhard Pfahringer, Eibe Frank, Henry Gouk, 2019, ACML.

MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes

Bernhard Pfahringer, Michael J. Cree, Eibe Frank, 2018, ECML/PKDD.

MOA: Massive Online Analysis

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, J. Mach. Learn. Res..

Dynamic and Heterogeneous Ensembles for Time Series Forecasting

Luís Torgo, Mariana Oliveira, Bernhard Pfahringer, 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Adaptive random forests for evolving data stream classification

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2017, Machine Learning.

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

A. Bifet, B. Pfahringer, Yaqian Zhang, 2022, 2209.13917.

Towards a Framework for Designing Full Model Selection and Optimization Systems

Quan Sun, Michael Mayo, Bernhard Pfahringer, 2013, MCS.

Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction

Bernhard Pfahringer, Felipe Bravo-Marquez, Arun Khanchandani, 2021, Cogn. Comput..

Optimizing the Induction of Alternating Decision Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2001, PAKDD.

Cooperative Coevolution

Charles X. Ling, T. Shultz, A. Mueen, 2010, Encyclopedia of Machine Learning and Data Mining.

Pruning Incremental Linear Model Trees with Approximate Lookahead

Stefan Kramer, Bernhard Pfahringer, Andreas Hapfelmeier, 2014, IEEE Transactions on Knowledge and Data Engineering.

Mining frequent closed graphs on evolving data streams

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2011, KDD.

Continual Learning

Charles X. Ling, T. Shultz, A. Mueen, 2010, Encyclopedia of Machine Learning.

Probability Calibration Trees pdf

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2017, ACML.

New ensemble methods for evolving data streams

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2009, KDD.

Better Self-training for Image Classification through Self-supervision pdf

Bernhard Pfahringer, Eibe Frank, Attaullah Sahito, 2021, AI.

Active Learning With Drifting Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2014, IEEE Transactions on Neural Networks and Learning Systems.

MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering

Thomas Seidl, Geoff Holmes, Albert Bifet, 2010, WAPA.

Extremely Fast Decision Tree Mining for Evolving Data Streams

Wei Fan, Geoff Holmes, Albert Bifet, 2017, KDD.

MEKA: A Multi-label/Multi-target Extension to WEKA

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2016, J. Mach. Learn. Res..

Experiments in cross-domain few-shot learning for image classification

B. Pfahringer, Hongyu Wang, G. Holmes, 2022, Journal of the Royal Society of New Zealand.

Boosting decision stumps for dynamic feature selection on data streams

Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, 2019, Inf. Syst..

A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

Jean Paul Barddal, Heitor Murilo Gomes, Bernhard Pfahringer, 2017, J. Syst. Softw..

Extending Explanation-Based Generalization

Bernhard Pfahringer, B. Pfahringer, 1989, ÖGAI.

Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data

Geoff Holmes, Jesse Read, Albert Bifet, 2012, IDA.

Building a Twitter opinion lexicon from automatically-annotated tweets

Felipe Bravo-Marquez, Bernhard Pfahringer, Eibe Frank, 2016, Knowl. Based Syst..

WEKA - Experiences with a Java Open-Source Project

Ian H. Witten, Geoff Holmes, Bernhard Pfahringer, 2010, J. Mach. Learn. Res..

Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, ACML.

Transferring sentiment knowledge between words and tweets

Felipe Bravo-Marquez, Bernhard Pfahringer, Eibe Frank, 2018, Web Intell..

On Dynamic Feature Weighting for Feature Drifting Data Streams

Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, 2016, ECML/PKDD.

Scalable and efficient multi-label classification for evolving data streams

Geoff Holmes, Jesse Read, Albert Bifet, 2012, Machine Learning.

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them

Geoff Holmes, Jesse Read, Albert Bifet, 2013, ECML/PKDD.

Efficient data stream classification via probabilistic adaptive windows

Geoff Holmes, Jesse Read, Albert Bifet, 2013, SAC '13.

New Options for Hoeffding Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2007, Australian Conference on Artificial Intelligence.

Multi-label classification using boolean matrix decomposition

Stefan Kramer, Bernhard Pfahringer, Jörg Wicker, 2012, SAC '12.

Change detection in categorical evolving data streams

Dino Ienco, Albert Bifet, Pascal Poncelet, 2014, SAC.

Compression-Based Evaluation of Partial Determinations

Stefan Kramer, Bernhard Pfahringer, Stefan Kramer, 1995, KDD.

Detecting Sentiment Change in Twitter Streaming Data

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2011, WAPA.

Feature Scoring using Tree-Based Ensembles for Evolving Data Streams

Heitor Murilo Gomes, Rodrigo Fernandes de Mello, Albert Bifet, 2019, 2019 IEEE International Conference on Big Data (Big Data).

Efficient Online Evaluation of Big Data Stream Classifiers

Albert Bifet, Gianmarco De Francisci Morales, Bernhard Pfahringer, 2015, KDD.

Evaluation methods and decision theory for classification of streaming data with temporal dependence

Geoff Holmes, Jesse Read, Albert Bifet, 2015, Machine Learning.

Ensembles of Restricted Hoeffding Trees

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2012, TIST.

Fast Perceptron Decision Tree Learning from Evolving Data Streams

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, PAKDD.

Improving Adaptive Bagging Methods for Evolving Data Streams

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2009, ACML.

A Toolbox for Learning from Relational Data with Propositional and Multi-instance Learners

Bernhard Pfahringer, Peter Reutemann, Eibe Frank, 2004, Australian Conference on Artificial Intelligence.

Iterative subset selection for feature drifting data streams

Jean Paul Barddal, Bernhard Pfahringer, Lanqin Yuan, 2018, SAC.

Combining active learning with concept drift detection for data stream mining

Bartosz Krawczyk, Michal Wozniak, Bernhard Pfahringer, 2018, 2018 IEEE International Conference on Big Data (Big Data).

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2015, 2015 IEEE International Conference on Data Mining.

Online Hyperparameter Optimization for Streaming Neural Networks

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

MOA Concept Drift Active Learning Strategies for Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, WAPA.

MOA: A Real-Time Analytics Open Source Framework

Thomas Seidl, Geoff Holmes, Jesse Read, 2011, ECML/PKDD.

Towards Automated Configuration of Stream Clustering Algorithms

Matthias Carnein, Heike Trautmann, Albert Bifet, 2019, PKDD/ECML Workshops.

Algorithm Selection on Data Streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2014, Discovery Science.

Showcasing the TAIAO project: providing resources for machine learning from images of New Zealand's natural environment

A. Bifet, B. Pfahringer, Jacob Montiel, 2022, Journal of the Royal Society of New Zealand.

On Calibration of Nested Dichotomies

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2019, PAKDD.

On the Calibration of Nested Dichotomies for Large Multiclass Tasks pdf

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2018, ArXiv.

Discovery Science - 13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010. Proceedings

Bernhard Pfahringer, Achim Hoffmann, Geoffrey Holmes, 2010, Discovery Science.

A survey of automatic de-identification of longitudinal clinical narratives pdf

Michael Mayo, Bernhard Pfahringer, Vithya Yogarajan, 2018, ArXiv.

Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams

Russel Pears, Albert Bifet, Bernhard Pfahringer, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Learning Distance Metrics for Multi-Label Classification

Bernhard Pfahringer, Michael J. Cree, Henry Gouk, 2016, ACML.

Adaptive Online Domain Incremental Continual Learning

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2022, ICANN.

Efficient Search for Strong Partial Determinations

Stefan Kramer, Bernhard Pfahringer, Stefan Kramer, 1996, KDD.

Maximum Common Subgraph based locally weighted regression

Stefan Kramer, Bernhard Pfahringer, Fabian Buchwald, 2012, SAC '12.

Controlling Constructive Induction in CIPF: An MDL Approach

Bernhard Pfahringer, B. Pfahringer, 1994, ECML.

From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach

Felipe Bravo-Marquez, Bernhard Pfahringer, Eibe Frank, 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

AffectiveTweets: a Weka Package for Analyzing Affect in Tweets

Saif Mohammad, Bernhard Pfahringer, Eibe Frank, 2019, J. Mach. Learn. Res..

Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis

Felipe Bravo-Marquez, Bernhard Pfahringer, Eibe Frank, 2016, ECAI.

Eecient Search for Strong Partial Determinations

Bernhard Pfahringer, Stefan Kramer, B. Pfahringer, 1996 .

Compression-Based Feature Subset Selection

Bernhard Pfahringer, B. Pfahringer, 2007 .

Efficient multi-label classification for evolving data streams

Geoffrey Holmes, Jesse Read, Albert Bifet, 2010 .

Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes pdf

Jacob Montiel, Bernhard Pfahringer, Tony Smith, 2020, ArXiv.

Case study on bagging stable classifiers for data streams

Joaquin Vanschoren, Jan N. van Rijn, Bernhard Pfahringer, 2015 .

confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms

Matthias Carnein, Heike Trautmann, Albert Bifet, 2020, LION.

The online performance estimation framework: heterogeneous ensemble learning for data streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2017, Machine Learning.

Transformers for Multi-label Classification of Medical Text: An Empirical Comparison

Jacob Montiel, Bernhard Pfahringer, Vithya Yogarajan, 2021, AIME.

Clustering Based Active Learning for Evolving Data Streams

Dino Ienco, Albert Bifet, Bernhard Pfahringer, 2013, Discovery Science.

A Two-Stage Tree based Meta-Classifier using Stack-Generalization

I. Witten, B. Pfahringer, C. Giraud-Carrier, 2020 .

Combining Static and Dynamic Analysis to Improve Machine Learning-based Malware Classification

Heitor Murilo Gomes, Bernhard Pfahringer, Rajchada Chanajitt, 2021, 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA).

Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets

Felipe Bravo-Marquez, Bernhard Pfahringer, Eibe Frank, 2015, IJCAI.

Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems

Yun Sing Koh, Patricia Riddle, Bernhard Pfahringer, 2018, ECML/PKDD.

On Ensemble Techniques for Data Stream Regression

Albert Bifet, Heitor Murilo Gomes, Jacob Montiel, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Relational Random Forests Based on Random Relational Rules

Bernhard Pfahringer, Grant Anderson, B. Pfahringer, 2009, IJCAI.

Tell me who can learn you and I can tell you who you are: Landmarking Various Learning Algorithms

Hilan Bensusan, Bernhard Pfahringer, B. Pfahringer, 2000 .

Active Learning with Evolving Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, ECML/PKDD.

Machine Learning (In) Security: A Stream of Problems

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2020, Digital Threats: Research and Practice.

Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality

Albert Bifet, Bernhard Pfahringer, Eibe Frank, 2021, ECML/PKDD.

Survey on Online Streaming Continual Learning

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2023, IJCAI.

Adaptive Neural Networks for Online Domain Incremental Continual Learning

Heitor Murilo Gomes, A. Bifet, B. Pfahringer, 2022, DS.

Improving the performance of bagging ensembles for data streams through mini-batching pdf

Heitor Murilo Gomes, Albert Bifet, Hermes Senger, 2021, Inf. Sci..

Experiment databases

Geoff Holmes, Hendrik Blockeel, Joaquin Vanschoren, 2012, Machine Learning.

Bagging Ensemble Selection

Quan Sun, Bernhard Pfahringer, B. Pfahringer, 2011, Australasian Conference on Artificial Intelligence.

Learning Through Utility Optimization in Regression Tasks

Luís Torgo, Rita P. Ribeiro, Bernhard Pfahringer, 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Prediction of Ordinal Classes Using Regression Trees

Gerhard Widmer, Stefan Kramer, Bernhard Pfahringer, 2001, Fundam. Informaticae.

PolyLM: Learning about Polysemy through Language Modeling

Bernhard Pfahringer, Alan Ansell, Felipe Bravo-Marquez, 2021, EACL.

Determining Word-Emotion Associations from Tweets by Multi-label Classification

Saif Mohammad, Felipe Bravo-Marquez, Bernhard Pfahringer, 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric?

Michael Mayo, Bernhard Pfahringer, Vithya Yogarajan, 2019, Appl. Artif. Intell..

Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning

B. Pfahringer, Anton Lee, Yaqian Zhang, 2023, CIKM.