9 citations · 15 across the 4 of their papers we have counts for
5 papers · 1 filter
Automated Imbalanced Classification via Layered Learning
Vitor Cerqueira, Luis Torgo, Paula Branco +1
In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach t…
On the combined effect of class imbalance and concept complexity in deep learning
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo +2
Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions.…
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
Michał Koziarski, Colin Bellinger, Michał Woźniak
Real-world classification domains, such as medicine, health and safety, and finance, often exhibit imbalanced class priors and have asynchronous misclassification costs. In such ca…
ReMix: Calibrated Resampling for Class Imbalance in Deep learning
Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz
Class imbalance is a problem of significant importance in applied deep learning where trained models are exploited for decision support and automated decisions in critical areas su…
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Colin Bellinger, Rory Coles, Mark Crowley +1
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for example, the design of…