{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T10:40:00Z","timestamp":1742380800994},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[1997,12]]},"abstract":"<jats:p> Classification involves associating instances with particular classes by maximizing intra-class similarities and minimizing inter-class similarities. Thus, the way similarity among instances is measured is crucial for the success of the system. In case-based reasoning, it is assumed that similar problems have similar solutions. The case-based approach to classification is founded on retrieving cases from the case base that are similar to a given problem, and associating the problem with the class containing the most similar cases. <\/jats:p><jats:p> Similarity-based retrieval tools can advantageously be used in building flexible retrieval and classification systems. Case-based classification uses previously classified instances to label unknown instances with proper classes. Classification accuracy is affected by the retrieval process \u2013 the more relevant the instances used for classification, the greater the accuracy. <\/jats:p><jats:p> The paper presents a novel approach to case-based classification. The algorithm is based on a notion of similarity assessment and was developed for supporting flexible retrieval of relevant information. Case similarity is assessed with respect to a given context that defines constraints for matching. Context relaxation and restriction is used for controlling the classification accuracy. The validity of the proposed approach is tested on real-world domains, and the system's performance, in terms of accuracy and scalability, is compared to that of other machine learning algorithms. <\/jats:p>","DOI":"10.1142\/s0218213097000268","type":"journal-article","created":{"date-parts":[[2003,10,22]],"date-time":"2003-10-22T09:26:17Z","timestamp":1066814777000},"page":"511-536","source":"Crossref","is-referenced-by-count":8,"title":["Improving Performance of Case-Based Classification Using Context-Based Relevance"],"prefix":"10.1142","volume":"06","author":[{"given":"Igor","family":"Jurisica","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Toronto, Toronto, Ontario M5S 1A4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janice","family":"Glasgow","sequence":"additional","affiliation":[{"name":"Department of Computing and Information Sciences, Queen's University, Kingston, Ontario K7L 3N6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213097000268","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T03:15:06Z","timestamp":1565147706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213097000268"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1997,12]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[1997,12]]}},"alternative-id":["10.1142\/S0218213097000268"],"URL":"https:\/\/doi.org\/10.1142\/s0218213097000268","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[1997,12]]}}}