{"title":"一种新的CRM稳健分类方法","authors":"Xiaoyu Li, Changzheng He, P. Liatsis","doi":"10.1109/APWCS.2010.25","DOIUrl":null,"url":null,"abstract":"Customer classification is a key step in customer relationship management (CRM), and there are many methods used for it, such as Neural Net, association rules, SOM model, etc. However, most existing methods don’t take noise which is very common in reality into consideration. In this paper, we combine Croup Method of Data Handling (GMDH) with Takagi and Sugeno fuzzy model (TS) to form a new classification method TS-GMDH. The experimental result shows that TS-GMDH outperforms the benchmark classifiers when the noise level is high.","PeriodicalId":354322,"journal":{"name":"2010 Asia-Pacific Conference on Wearable Computing Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Robust Classification Method for CRM\",\"authors\":\"Xiaoyu Li, Changzheng He, P. Liatsis\",\"doi\":\"10.1109/APWCS.2010.25\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Customer classification is a key step in customer relationship management (CRM), and there are many methods used for it, such as Neural Net, association rules, SOM model, etc. However, most existing methods don’t take noise which is very common in reality into consideration. In this paper, we combine Croup Method of Data Handling (GMDH) with Takagi and Sugeno fuzzy model (TS) to form a new classification method TS-GMDH. The experimental result shows that TS-GMDH outperforms the benchmark classifiers when the noise level is high.\",\"PeriodicalId\":354322,\"journal\":{\"name\":\"2010 Asia-Pacific Conference on Wearable Computing Systems\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Asia-Pacific Conference on Wearable Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APWCS.2010.25\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Asia-Pacific Conference on Wearable Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWCS.2010.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Customer classification is a key step in customer relationship management (CRM), and there are many methods used for it, such as Neural Net, association rules, SOM model, etc. However, most existing methods don’t take noise which is very common in reality into consideration. In this paper, we combine Croup Method of Data Handling (GMDH) with Takagi and Sugeno fuzzy model (TS) to form a new classification method TS-GMDH. The experimental result shows that TS-GMDH outperforms the benchmark classifiers when the noise level is high.
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