{"title":"基于多层异构集成方法的欺诈检测","authors":"Haritha Rajeev Neenu Kuriakose","doi":"10.46501/0706001","DOIUrl":null,"url":null,"abstract":"Fraudulent detection is a large number of exercises that try to keep cash or property out of the way. Fraud\nsurveillance is used in many businesses such as banking or security. At the bank, misrepresentation may\ninvolve producing checks or using a Credit Card taken. Different types of robberies can include misfortune or\ncreate a problem with the expectation of only a paid Layer Ensemble Method running other AI fields including\ncollecting learning. Recently, there have been one deep group models deployed with a large number of\nclassifiers in each layer. These models, as a result, require a much larger calculation. In addition, the deep\nintegration models are available that use all the separating elements including the unnecessary ones that\ncan reduce the accuracy of the group. In this experiment, we propose a multi-layered learning structure called\nthe Two-Layer Ensemble System to address the issue of definition. The proposed framework is working with\na number of weird filters to get the troupe jumper sity, in these lines being a technology in the use of\nequipment.","PeriodicalId":13741,"journal":{"name":"International Journal for Modern Trends in Science and Technology","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fraud Detection Using Multi-layer Heterogeneous\\nEnsembleMethod\",\"authors\":\"Haritha Rajeev Neenu Kuriakose\",\"doi\":\"10.46501/0706001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fraudulent detection is a large number of exercises that try to keep cash or property out of the way. Fraud\\nsurveillance is used in many businesses such as banking or security. At the bank, misrepresentation may\\ninvolve producing checks or using a Credit Card taken. Different types of robberies can include misfortune or\\ncreate a problem with the expectation of only a paid Layer Ensemble Method running other AI fields including\\ncollecting learning. Recently, there have been one deep group models deployed with a large number of\\nclassifiers in each layer. These models, as a result, require a much larger calculation. In addition, the deep\\nintegration models are available that use all the separating elements including the unnecessary ones that\\ncan reduce the accuracy of the group. In this experiment, we propose a multi-layered learning structure called\\nthe Two-Layer Ensemble System to address the issue of definition. The proposed framework is working with\\na number of weird filters to get the troupe jumper sity, in these lines being a technology in the use of\\nequipment.\",\"PeriodicalId\":13741,\"journal\":{\"name\":\"International Journal for Modern Trends in Science and Technology\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal for Modern Trends in Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46501/0706001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Modern Trends in Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46501/0706001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fraud Detection Using Multi-layer Heterogeneous
EnsembleMethod
Fraudulent detection is a large number of exercises that try to keep cash or property out of the way. Fraud
surveillance is used in many businesses such as banking or security. At the bank, misrepresentation may
involve producing checks or using a Credit Card taken. Different types of robberies can include misfortune or
create a problem with the expectation of only a paid Layer Ensemble Method running other AI fields including
collecting learning. Recently, there have been one deep group models deployed with a large number of
classifiers in each layer. These models, as a result, require a much larger calculation. In addition, the deep
integration models are available that use all the separating elements including the unnecessary ones that
can reduce the accuracy of the group. In this experiment, we propose a multi-layered learning structure called
the Two-Layer Ensemble System to address the issue of definition. The proposed framework is working with
a number of weird filters to get the troupe jumper sity, in these lines being a technology in the use of
equipment.