546 citations · 945 across the 12 of their papers we have counts for
13 papers
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (r…
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan +2
One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learnin…
Improving Position Encoding of Transformers for Multivariate Time Series Classification
Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb +1
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to…
Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series
Matthieu Herrmann, Chang Wei Tan, Mahsa Salehi +1
Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance,…
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan +3
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer…
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled…
Deep Learning for Time Series Anomaly Detection: A Survey
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan +2
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate n…
Emergent and Unspecified Behaviors in Streaming Decision Trees
Chaitanya Manapragada, Geoffrey I Webb, Mahsa Salehi +1
Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams. These very fast decision trees are used in many real applications where data i…
An Eager Splitting Strategy for Online Decision Trees
Chaitanya Manapragada, Heitor M Gomes, Mahsa Salehi +2
Decision tree ensembles are widely used in practice. In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online t…
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network
Chang Wei Tan, Mahsa Salehi, Geoffrey Mackellar
In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety. We use a commercial wireless headset that g…
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets
Shangqi Lai, Xingliang Yuan, Amin Sakzad +3
Outlier detection is widely used in practice to track the anomaly on incremental datasets such as network traffic and system logs. However, these datasets often involve sensitive i…
Online Semi-Supervised Concept Drift Detection with Density Estimation
Chang How Tan, Vincent CS Lee, Mahsa Salehi
Concept drift is formally defined as the change in joint distribution of a set of input variables X and a target variable y. The two types of drift that are extensively studied are…
Extremely Fast Decision Tree
Chaitanya Manapragada, Geoff Webb, Mahsa Salehi
We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. W…