In this paper we proposed an approach to maintain large case library, which based on the idea that a large case library can be transformed to a compact one by using a set of case-specific weights. A linear programming technique is being used to obtain case-specific weights. By learning such local weights knowledge, many of redundant or similar cases can be removed from the original case library or stored in a secondary case library, This approach is useful for case library with a large number of redundant or similar cases and the retrieval efficiency is a real concern of the user. This method of maintaining case library from scratch, as proposed in this paper, consists of two main steps. First, a linear programming technique for learning case-specific weights is used to evaluate the importance of different features for each case. Second, a case selection strategy based on the concepts of case coverage and reachability is carried out to select representative cases, Furthermore, a case retrieval strategy of the compact case library we built is discussed. The effectiveness of the approach is demonstrated experimentally by using two sets of testing data, and the results are promising.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||4th Mexican International Conference on Artificial Intelligence, MICAI 2005|
|Period||14/11/05 → 18/11/05|
- Theoretical Computer Science
- Computer Science(all)