C4.5: programs for machine learning
C4.5: programs for machine learning
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Software Architecture in Practice
Software Architecture in Practice
Jena: implementing the semantic web recommendations
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Full Bayesian network classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning Bayesian Networks
A fast decision tree learning algorithm
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Pronto: a non-monotonic probabilistic description logic reasoner
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Novel Applications Integrate Location and Context Information
IEEE Pervasive Computing
Decision trees: a recent overview
Artificial Intelligence Review
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The complexity of continuous care settings has increased due to an ageing population, a dwindling number of caregivers and increasing costs. Electronic healthcare (eHealth) solutions are often introduced to deal with these issues. This technological equipment further increases the complexity of healthcare as the caregivers are responsible for integrating and configuring these solutions to their needs. Small differences in user requirements often occur between various environments where the services are deployed. It is difficult to capture these nuances at development time. Consequently, the services are not tuned towards the users' needs. This paper describes our experiences with extending an eHealth application with self-learning components such that it can automatically adjust its parameters at run-time to the users' needs and preferences. These components gather information about the usage of the application. This collected information is processed by data mining techniques to learn the parameter values for the application. Each discovered parameter is associated with a probability, which expresses its reliability. Unreliable values are filtered. The remaining parameters and their reliability are integrated into the application. The eHealth application is the ontology-based Nurse Call System (oNCS), which assesses the priority of a call based on the current context and assigns the most appropriate caregiver to a call. Decision trees and Bayesian networks are used to learn and adjust the parameters of the oNCS. For a realistic dataset of 1050 instances, correct parameter values are discovered very efficiently as the components require at most 100ms execution time and 20MB memory.