A Case‐Addition Policy for Case‐Base Maintenance
Qiang Yang, Jun Zhu · Computational Intelligence · 2001
A major problem in many practical applications of case‐based reasoning (CBR) and knowledge reuse is how to keep the case bases concise and complete. To solve this problem requires repeated maintenance operations to be applied to case bases. Different maintenance policies may result in case bases with very different quality. In this article, we present a case‐addition maintenance policy that is guaranteed to return a concise case base with good coverage quality. We demonstrate that the coverage of the case base computed by the case‐addition algorithm is no worse than the optimal case‐base coverage by a fixed lower bound. We also show that the algorithm implementing the case‐addition policy is efficient. Our result also highlights benefit reduction as a key factor in influencing the convergence of case‐base coverage when cases are added to a case base. Through our theoretical analysis, we analytically derive the well known coverage convergence curves commonly displayed in CBR experiments and show that benefit reduction can be used as a predictor for convergence speed.