TY - CHAP
T1 - Maintenance by a committee of experts
T2 - 8th International Conference on Case-Based Reasoning, ICCBR 2009
AU - Cummins, Lisa
AU - Bridge, Derek
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/70350389169
U2 - 10.1007/978-3-642-02998-1_10
DO - 10.1007/978-3-642-02998-1_10
M3 - Chapter
AN - SCOPUS:70350389169
SN - 3642029973
SN - 9783642029974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 120
EP - 134
BT - Case-Based Reasoning Research and Development - 8th International Conference on Case-Based Reasoning, ICCBR 2009, Proceedings
Y2 - 20 July 2009 through 23 July 2009
ER -