{"title":"Application of SVM Based on Rough Set in Real Estate Investment Environment Comprehensive Evaluation","authors":"Ting Wang, Yanqing Li","doi":"10.1109/ICRMEM.2008.22","DOIUrl":null,"url":null,"abstract":"The stable prices rose in the real estate market attracted a large amount of funds injected into it, to choose a good investment environment has been a keyto get profit from investment. In this paper, a Support Vector Machine (SVM) model is founded to do the evaluation. Based on the comprehensive evaluation index system of real estate investment environment, Rough set (RS) is introduced to reduce numbers of evaluation indicators, thus reducing the dimensions of the input space of SVM., when treating the reduced data as the input space of SVM, we find that both the convergence speed and the classify accuracy are enhanced in comparison with the general SVM comprehensive evaluation method and BP evaluation method.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Risk Management & Engineering Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRMEM.2008.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
The stable prices rose in the real estate market attracted a large amount of funds injected into it, to choose a good investment environment has been a keyto get profit from investment. In this paper, a Support Vector Machine (SVM) model is founded to do the evaluation. Based on the comprehensive evaluation index system of real estate investment environment, Rough set (RS) is introduced to reduce numbers of evaluation indicators, thus reducing the dimensions of the input space of SVM., when treating the reduced data as the input space of SVM, we find that both the convergence speed and the classify accuracy are enhanced in comparison with the general SVM comprehensive evaluation method and BP evaluation method.
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