275 citations · 551 across the 32 of their papers we have counts for
7 papers · 1 filter
Improving the performance of bagging ensembles for data streams through mini-batching
Guilherme Cassales, Heitor Gomes, Albert Bifet +2
Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and tra…
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents
Vithya Yogarajan, Bernhard Pfahringer, Tony Smith +1
Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classificati…
Predicting COVID-19 Patient Shielding: A Comprehensive Study
Vithya Yogarajan, Jacob Montiel, Tony Smith +1
There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention…
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…
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
Attaullah Sahito, Eibe Frank, Bernhard Pfahringer
Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for tra…
Better Self-training for Image Classification through Self-supervision
Attaullah Sahito, Eibe Frank, Bernhard Pfahringer
Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the train…
PolyLM: Learning about Polysemy through Language Modeling
Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer
To avoid the "meaning conflation deficiency" of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks su…