275 citations · 345 across the 6 of their papers we have counts for
9 papers · 1 filter
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…
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…