{"title":"基于模糊集和粗糙集的数值数据预测器","authors":"Chih-Ching Hsiao, Yi-Wei Ku","doi":"10.1109/IWACI.2010.5585153","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a fuzzy predictor which fuzzy rules are generated directly from numerical data pairs. Unfortunately, the fuzzy rules may be increase growing to extra numbers, especially the data pairs contain noise or outlier. The Fuzzy-rough feature selection will be introduced for those fuzzy rules reduction. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule will be adjusted by fine tuning.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"151 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A predictor from numerical data based on fuzzy sets and rough sets\",\"authors\":\"Chih-Ching Hsiao, Yi-Wei Ku\",\"doi\":\"10.1109/IWACI.2010.5585153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a fuzzy predictor which fuzzy rules are generated directly from numerical data pairs. Unfortunately, the fuzzy rules may be increase growing to extra numbers, especially the data pairs contain noise or outlier. The Fuzzy-rough feature selection will be introduced for those fuzzy rules reduction. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule will be adjusted by fine tuning.\",\"PeriodicalId\":189187,\"journal\":{\"name\":\"Third International Workshop on Advanced Computational Intelligence\",\"volume\":\"151 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Workshop on Advanced Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWACI.2010.5585153\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Workshop on Advanced Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWACI.2010.5585153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A predictor from numerical data based on fuzzy sets and rough sets
In this paper, we propose a fuzzy predictor which fuzzy rules are generated directly from numerical data pairs. Unfortunately, the fuzzy rules may be increase growing to extra numbers, especially the data pairs contain noise or outlier. The Fuzzy-rough feature selection will be introduced for those fuzzy rules reduction. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule will be adjusted by fine tuning.
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