5 citations · 7 across the 6 of their papers we have counts for
7 papers
Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information
Ben Halstead, Yun Sing Koh, Patricia Riddle +3
Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the…
A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams
Ben Halstead, Yun Sing Koh, Patricia Riddle +2
The distribution of streaming data often changes over time as conditions change, a phenomenon known as concept drift. Only a subset of previous experience, collected in similar con…
GTree: GPU-Friendly Privacy-preserving Decision Tree Training and Inference
Qifan Wang, Shujie Cui, Lei Zhou +4
Decision tree (DT) is a widely used machine learning model due to its versatility, speed, and interpretability. However, for privacy-sensitive applications, outsourcing DT training…
What You See is Not What You Get: The Role of Email Presentation in Phishing Susceptibility
Sijie Zhuo, Robert Biddle, Lucas Betts +4
Phishing is one of the most prevalent social engineering attacks that targets both organizations and individuals. It is crucial to understand how email presentation impacts users'…
SoK: Human-Centered Phishing Susceptibility
Sijie Zhuo, Robert Biddle, Yun Sing Koh +2
Phishing is recognised as a serious threat to organisations and individuals. While there have been significant technical advances in blocking phishing attacks, people remain the la…
Evaluating Software User Feedback Classifiers on Unseen Apps, Datasets, and Metadata
Peter Devine, Yun Sing Koh, Kelly Blincoe
Listening to user's requirements is crucial to building and maintaining high quality software. Online software user feedback has been shown to contain large amounts of information…
Recurring Concept Meta-learning for Evolving Data Streams
Robert Anderson, Yun Sing Koh, Gillian Dobbie +1
When concept drift is detected during classification in a data stream, a common remedy is to retrain a framework's classifier. However, this loses useful information if the classif…