Adaptation-Guided Feature Deletion: Testing Recoverability to Guide Case Compression

Extensive case-based reasoning research has studied methods for generating compact, competent case bases. This work has focused primarily on compressing the case base by deleting entire cases, based solely on their competence contributions. Recent work proposed an alternative which compressed individual cases by selectively deleting their internal contents. Early studies of this approach, termed flexible feature deletion (FFD), demonstrated that for suitable domains, such as domains with cases of varying sizes for which case usefulness can be retained despite internal deletions, even very simple FFD approaches may outperform standard per-case methods. However, more sophisticated methods are needed. Because FFD’s internal changes to cases can be seen as a form of case adaptation, this paper investigates whether the adaptation knowledge of a system can be harnessed to improve FFD. This paper proposes tying FFD choices directly to adaptation knowledge and presents results on a competence-preserving FFD method which prioritizes feature deletions by the recoverability of deleted features through case adaptation. Evaluation of recoverability-based FFD in a path-finding domain supports that it provides superior competence retention compared to standard flexible feature deletion at the same level of compression.