@inproceedings{SmythKeane95,
title = {{Remembering to forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems}},
author = {Barry Smyth and Mark Keane},
booktitle = {Proceedings of the 13th International Joint Conference on Artificial Intelligence},
pages = {377--382},
year = {1995},
abstract = {The utility problem occurs when the cost associated with
searching for relevant knowledge outweighs the benefit of
applying this knowledge. One common machine learning strategy for
coping with this problem ensures that stored knowledge is
genuinely useful, deleting any structures that do not contribute
to performance in a positive sense, and essentially limiting the
size of the knowledge-base. We will examine this deletion
strategy in the context of case-based reasoning (CBR) systems.
In CBR the impact of the utility problem is very much dependant
on the size and growth of the case-base; larger case-bases mean
more expensive retrieval stages, an expensive overhead in CBR
systems. Traditional deletion strategies will keep performance in
check (and thereby control the classical utility problem) but
they may cause problems for CBR system competence. This effect is
demonstrated experimentally and in reply two new deletion
strategies are proposed that can take both competence and
performance into consideration during deletion.},
keywords = {,imported, CBR Maintenance barry-smyth }
}
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