Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Case-Based Design for Tablet Formulation
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Case-Based Reasoning Technology, From Foundations to Applications
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
A hybrid genetic algorithm for classification
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Learning to Adapt for Case-Based Design
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Learning and Applying Case-Based Adaptation Knowledge
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Defining Similarity Measures: Top-Down vs. Bottom-Up
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Boosting CBR Agents with Genetic Algorithms
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Introducing attribute risk for retrieval in case-based reasoning
Knowledge-Based Systems
Integrating case-based reasoning with an electronic patient record system
Artificial Intelligence in Medicine
Learning fuzzy rules for similarity assessment in case-based reasoning
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A case-based reasoning approach to formulating university timetables using genetic algorithms
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Unsupervised feature selection for text data
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Discovering knowledge about key sequences for indexing time series cases in medical applications
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
The utility problem for lazy learners - towards a non-eager approach
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
CBTV: visualising case bases for similarity measure design and selection
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis
Decision Support Systems
Fuzzy rule-based similarity model enables learning from small case bases
Applied Soft Computing
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Knowledge in a case-based reasoning (CBR) system is often more extensive than simply the cases, therefore knowledge engineering may still be very demanding. This paper offers a first step towards an automated knowledge acquisition and refinement tool for non-case CBR knowledge. A data-driven approach is presented where a Genetic Algorithm learns effective feature selection for inducing case-base index, and feature weights for similarity measure for case retrieval. The optimisation can be viewed as knowledge acquisition or maintenance depending on whether knowledge is being created or refined. Optimising CBR retrieval is achieved using cases from the case-base and only minimal expert input, and so can be easily applied to an evolving case-base or a changing environment. Experiments with a real tablet formulation problem show the gains of simultaneously optimising the index and similarity measure. Provided that the available data represents the problem domain well, the optimisation has good generalisation properties and the domain knowledge extracted is comparable to expert knowledge.