23 citations · 89 across the 19 of their papers we have counts for
7 papers · 1 filter
Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study
Claire Little, Mark Elliot, Richard Allmendinger +1
Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data. So far much of this work has been applied to use cases outside of the data…
Towards a fairer reimbursement system for burn patients using cost-sensitive classification
Chimdimma Noelyn Onah, Richard Allmendinger, Julia Handl +1
The adoption of the Prospective Payment System (PPS) in the UK National Health Service (NHS) has led to the creation of patient groups called Health Resource Groups (HRG). HRGs aim…
What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimization
Richard Allmendinger, Andrzej Jaszkiewicz, Arnaud Liefooghe +1
The difficulty of solving a multi-objective optimization problem is impacted by the number of objectives to be optimized. The presence of many objectives typically introduces a num…
Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models
Andreea Avramescu, Richard Allmendinger, Manuel López-Ibáñez
With almost 50% of annual commercial drug approvals being Personalised Medicine (PM) and its huge potential to improve quality of life, this emerging medical sector has received in…
Heterogeneous Objectives: State-of-the-Art and Future Research
Richard Allmendinger, Joshua Knowles
Multiobjective optimization problems with heterogeneous objectives are defined as those that possess significantly different types of objective function components (not just incomm…
HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis
Cameron Shand, Richard Allmendinger, Julia Handl +2
Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i)~the elusiveness of a unique mathematical definition of this unsupervised learning…
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
Youngmin Kim, Richard Allmendinger, Manuel López-Ibáñez
Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies…