{"title":"一种基于相似粗糙集的案例推理算法","authors":"S. Ji, Shenfang Yuan, Shui-ping Wang","doi":"10.1109/FSKD.2008.13","DOIUrl":null,"url":null,"abstract":"A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the SRS algorithm, based on similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental results have confirmed the algorithm feasibility and the validity.","PeriodicalId":208332,"journal":{"name":"2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Algorithm for Case-Based Reasoning Based on Similarity Rough Set\",\"authors\":\"S. Ji, Shenfang Yuan, Shui-ping Wang\",\"doi\":\"10.1109/FSKD.2008.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the SRS algorithm, based on similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental results have confirmed the algorithm feasibility and the validity.\",\"PeriodicalId\":208332,\"journal\":{\"name\":\"2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery\",\"volume\":\"99 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2008.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2008.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Algorithm for Case-Based Reasoning Based on Similarity Rough Set
A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the SRS algorithm, based on similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental results have confirmed the algorithm feasibility and the validity.
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