C4.5: programs for machine learning
C4.5: programs for machine learning
Dimensions of knowledge sharing and reuse
Computers and Biomedical Research
Machine Learning
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Machine Learning
Eliciting Knowledge and Transferring It Effectively to a Knowledge-Based System
IEEE Transactions on Knowledge and Data Engineering
Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Evaluation of decision trees: a multi-criteria approach
Computers and Operations Research
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
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
Handbook on Ontologies
Web Semantics: Science, Services and Agents on the World Wide Web
Data Mining in Biomedicine Using Ontologies
Data Mining in Biomedicine Using Ontologies
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Learning interpretable fuzzy inference systems with FisPro
Information Sciences: an International Journal
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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In many fields involving complex environments or living organisms, data-driven models are useful to make simulations in order to extrapolate costly experiments and to design decision-support tools. Learning methods can be used to build interpretable models from data. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modeled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. Another issue is to design relevant ontologies to formalize complex knowledge. Interpretable predictive models can help in this matter. In this paper, we propose a generic iterative approach to design ontology-aware and relevant data-driven models. It is based upon an ontology to model the domain knowledge and a learning method to build the interpretable models (decision trees in this paper). Subjective and objective evaluations are both involved in the process. A case study in the domain of Food Industry demonstrates the interest of this approach.