Sean Stijven

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Feature selection in high-dimensional data sets is an open problem with no universal satisfactory method available. In this paper we discuss the requirements for such a method with respect to the various aspects of feature importance and explore them using regression random forests and symbolic regression. We study 'conventional' feature selection with both(More)
Infrastructure-as-a-Service (IaaS) cloud providers offer a number of different tariff structures. The user has to balance the flexibility of the often quoted pay-by-the-hour, fixed price (" on demand ") model against the lower-cost-per-hour rate of a " reserved contract ". These tariff structures offer a significantly reduced cost per server hour (up to(More)
Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and(More)
Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations. We have found large performance differences with a discrete-time ABM for(More)
INTRODUCTION The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient ICU resource usage and varies considerably. Planning of postoperative ICU admissions is important as ICUs often have no nonoccupied beds available. PROBLEM STATEMENT Estimation of the ICU bed availability for the next coming days is(More)
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