C4.5: programs for machine learning
C4.5: programs for machine learning
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Machine Learning
Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Ontology based complex object recognition
Image and Vision Computing
Expert-Driven Knowledge Discovery
ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
Conceptual Graphs as Cooperative Formalism to Build and Validate a Domain Expertise
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
Web Semantics: Science, Services and Agents on the World Wide Web
Hi-index | 0.00 |
When using data-driven models to make simulations and predictions in experimental sciences, it is essential for the domain expert to be confident about the predicted values. Increasing this confidence can be done by using interpretable models, so that the expert can follow the model reasoning pattern, and by integrating expert knowledge to the model itself. New pieces of useful formalised knowledge can then be integrated to an existing corpus while data-driven models are tuned according to the expert advice. In this paper, we propose a generic interactive procedure, relying on an ontology to model qualitative knowledge and on decision trees as a data-driven rule learning method. A case study based on data issued from multiple scientific papers in the field of cereal transformation illustrates the approach.