Maintenance by a committee of experts: The MACE approach to case-base maintenance

Abstract

Case-base administrators face a choice of many maintenance algorithms. It is well-known that these algorithms have different biases that cause them to perform inconsistently over different datasets. In this paper, we demonstrate some of the biases of the most commonly-used maintenance algorithms. This motivates our new approach: maintenance by a committee of experts (MACE). We create composite algorithms that comprise more than one individual maintenance algorithm in the hope that the strengths of one algorithm will compensate for the weaknesses of another. In MACE, we combine algorithms in two ways: either we put them in sequence so that one runs after the other, or we allow them to run separately and then vote as to whether a case should be deleted or not. We define a grammar that describes how these composites are created. We perform experiments based on 27 diverse datasets. Our results show that the MACE approach allows us to define algorithms with different trade-offs between accuracy and the amount of deletion. © 2009 Springer Berlin Heidelberg.