Model Adaptation · Software component
Case-Based Rule Refiner
Software componentModel AdaptationModelsarc:CaseBasedRuleRefiner
A rule learner that analyses accumulated misclassified cases to propose added conditions or new rules that correct an existing expert rule set.
Responsibility. Proposes corrective refinements to existing rules from their failure cases.
Also known as: Rule refinement algorithm
Variant of Rule Learner abstract
When to choose. Choose when an expert-provided initial rule set exists and should be refined from accumulated cases where its decisions proved wrong.
Relationships
reads dependency
is triggered by dynamic
alternative to variability
Design guidance
- SHOULD accumulate multiple failure cases before proposing a change so refinements address genuine patterns, not noise.
Quantitative guidance
As stated by the sources; verify before use.
- Medical case: 234 discrepancy cases accumulated over six months; refined rule would have caught 43 of 52 elderly false negatives (Ch5.11).
Classification
- Patterns
- Case-based refinementOutcome-driven rule refinement
- Risks mitigated
- Overfitting rules to exceptional cases
Sources
- Ch5.11: T. Nguyen, "Rule-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.11. ISBN: 9798244538229.