{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T18:10:14Z","timestamp":1700417414937},"reference-count":0,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2002,12,17]],"date-time":"2002-12-17T00:00:00Z","timestamp":1040083200000},"content-version":"vor","delay-in-days":595,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence"],"published-print":{"date-parts":[[2001,5]]},"abstract":"The knowledge stored in a case base is central to the problem solving of a case\u2010based reasoning (CBR) system. Therefore, case\u2010base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections\/weights for the indexing and nearest\u2010neighbor algorithms used by CBR retrieval. Optimization is applied after case\u2010base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case\u2010base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.<\/jats:p>","DOI":"10.1111\/0824-7935.00149","type":"journal-article","created":{"date-parts":[[2003,3,11]],"date-time":"2003-03-11T13:06:42Z","timestamp":1047388002000},"page":"346-363","source":"Crossref","is-referenced-by-count":17,"title":["Maintaining Retrieval Knowledge in a Case\u2010Based Reasoning System"],"prefix":"10.1111","volume":"17","author":[{"given":"Susan","family":"Craw","sequence":"first","affiliation":[]},{"given":"Jacek","family":"Jarmulak","sequence":"additional","affiliation":[]},{"given":"Ray","family":"Rowe","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2002,12,17]]},"container-title":["Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2F0824-7935.00149","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/0824-7935.00149","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T17:30:25Z","timestamp":1700415025000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/0824-7935.00149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2001,5]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2001,5]]}},"alternative-id":["10.1111\/0824-7935.00149"],"URL":"http:\/\/dx.doi.org\/10.1111\/0824-7935.00149","archive":["Portico"],"relation":{},"ISSN":["0824-7935","1467-8640"],"issn-type":[{"value":"0824-7935","type":"print"},{"value":"1467-8640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2001,5]]}}}
