275 citations · 642 across the 23 of their papers we have counts for
12 papers · 1 filter
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank +3
Online continual learning (OCL) aims to train neural networks incrementally from a non-stationary data stream with a single pass through data. Rehearsal-based methods attempt to ap…
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum
Zac Pullar-Strecker, Katharina Dost, Eibe Frank +1
Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional supervised learning. An active learner selects th…
Semi-Supervised Learning using Siamese Networks
Attaullah Sahito, Eibe Frank, Bernhard Pfahringer
Neural networks have been successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are…
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles
Rory Mitchell, Eibe Frank, Geoffrey Holmes
SHAP (SHapley Additive exPlanation) values provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values. While exact calculation of…
Adaptive XGBoost for Evolving Data Streams
Jacob Montiel, Rory Mitchell, Eibe Frank +3
Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is e…
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation
Rhys Compton, Eibe Frank, Panos Patros +1
Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a n…
Classifier Chains: A Review and Perspectives
Jesse Read, Bernhard Pfahringer, Geoff Holmes +1
The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves linking together off-the-shelf…
On the Calibration of Nested Dichotomies for Large Multiclass Tasks
Tim Leathart, Eibe Frank, Bernhard Pfahringer +1
Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A tree structure is induced that recursively splits th…
Probability Calibration Trees
Tim Leathart, Eibe Frank, Geoffrey Holmes +1
Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications. Exi…
Ensembles of Nested Dichotomies with Multiple Subset Evaluation
Tim Leathart, Eibe Frank, Bernhard Pfahringer +1
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the…
XGBoost: Scalable GPU Accelerated Learning
Rory Mitchell, Andrey Adinets, Thejaswi Rao +1
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GP…
Locally Weighted Naive Bayes
Eibe Frank, Mark Hall, Bernhard Pfahringer
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these…