Xintao Qiu, Dongmei Fu, Zhenduo Fu, K. Říha, Radim Burget
{"title":"基于SVM-RFE的材料腐蚀建模与特征选择方法","authors":"Xintao Qiu, Dongmei Fu, Zhenduo Fu, K. Říha, Radim Burget","doi":"10.1109/TSP.2011.6043693","DOIUrl":null,"url":null,"abstract":"Material corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the modeling and feature selection of Material corrosion data. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing. By combining RFE and SVM, a novel feature selection method SVM-RFE is introduced. Then integrating this feature selection method and SVM modeling method, a special modeling framework is built. According to the experiments, the priority of this method is established not only on algorithm efficiency but also on predicting precision.","PeriodicalId":341695,"journal":{"name":"2011 34th International Conference on Telecommunications and Signal Processing (TSP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"The method for material corrosion modelling and feature selection with SVM-RFE\",\"authors\":\"Xintao Qiu, Dongmei Fu, Zhenduo Fu, K. Říha, Radim Burget\",\"doi\":\"10.1109/TSP.2011.6043693\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Material corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the modeling and feature selection of Material corrosion data. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing. By combining RFE and SVM, a novel feature selection method SVM-RFE is introduced. Then integrating this feature selection method and SVM modeling method, a special modeling framework is built. According to the experiments, the priority of this method is established not only on algorithm efficiency but also on predicting precision.\",\"PeriodicalId\":341695,\"journal\":{\"name\":\"2011 34th International Conference on Telecommunications and Signal Processing (TSP)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 34th International Conference on Telecommunications and Signal Processing (TSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TSP.2011.6043693\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 34th International Conference on Telecommunications and Signal Processing (TSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TSP.2011.6043693","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The method for material corrosion modelling and feature selection with SVM-RFE
Material corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the modeling and feature selection of Material corrosion data. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing. By combining RFE and SVM, a novel feature selection method SVM-RFE is introduced. Then integrating this feature selection method and SVM modeling method, a special modeling framework is built. According to the experiments, the priority of this method is established not only on algorithm efficiency but also on predicting precision.