多类数据特征选择新方法:迭代加权AUC (IWA)

Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems Pub Date : 2011-11-10 DOI:10.1109/IDAACS.2011.6072769

P. Honzík, P. Kucera, O. Hyncica, Daniel Haupt

{"title":"多类数据特征选择新方法:迭代加权AUC (IWA)","authors":"P. Honzík, P. Kucera, O. Hyncica, Daniel Haupt","doi":"10.1109/IDAACS.2011.6072769","DOIUrl":null,"url":null,"abstract":"This paper deals with the new filter feature selection method Iteratively Weighted Area under Receiver Operating Characteristic (IWA). It is aimed for the multi-class problems with quantitative inputs. The experiments prove its superior quality in comparison to the equivalent methods.","PeriodicalId":106306,"journal":{"name":"Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"New feature selection method for multi-class data: Iteratively weighted AUC (IWA)\",\"authors\":\"P. Honzík, P. Kucera, O. Hyncica, Daniel Haupt\",\"doi\":\"10.1109/IDAACS.2011.6072769\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with the new filter feature selection method Iteratively Weighted Area under Receiver Operating Characteristic (IWA). It is aimed for the multi-class problems with quantitative inputs. The experiments prove its superior quality in comparison to the equivalent methods.\",\"PeriodicalId\":106306,\"journal\":{\"name\":\"Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IDAACS.2011.6072769\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IDAACS.2011.6072769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

本文研究了一种新的滤波器特征选择方法——接收机工作特性下迭代加权面积法(IWA)。它针对的是具有定量输入的多类问题。实验证明了该方法优于等效方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

New feature selection method for multi-class data: Iteratively weighted AUC (IWA)

This paper deals with the new filter feature selection method Iteratively Weighted Area under Receiver Operating Characteristic (IWA). It is aimed for the multi-class problems with quantitative inputs. The experiments prove its superior quality in comparison to the equivalent methods.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems

自引率

0.00%

发文量