{"title":"通过在数据中找到与响应相关的子组来解释分类","authors":"E. Parviainen, Aki Vehtari","doi":"10.1109/SNPD.2010.20","DOIUrl":null,"url":null,"abstract":"A method for explaining results of a regression based classifier is proposed. The data is clustered using a metric extracted from the classifier. This way, clusters found are related to classifier predictions, and each cluster can be considered a possible explanation for classification result. The clusters are described by simple rules, meant to be easy for a human to understand. The key points of the work are presenting a modular framework for explaining the classification, and studying and comparing two different approaches for extracting a metric from a classifier model.","PeriodicalId":266363,"journal":{"name":"2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Explaining Classification by Finding Response-Related Subgroups in Data\",\"authors\":\"E. Parviainen, Aki Vehtari\",\"doi\":\"10.1109/SNPD.2010.20\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method for explaining results of a regression based classifier is proposed. The data is clustered using a metric extracted from the classifier. This way, clusters found are related to classifier predictions, and each cluster can be considered a possible explanation for classification result. The clusters are described by simple rules, meant to be easy for a human to understand. The key points of the work are presenting a modular framework for explaining the classification, and studying and comparing two different approaches for extracting a metric from a classifier model.\",\"PeriodicalId\":266363,\"journal\":{\"name\":\"2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SNPD.2010.20\",\"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 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD.2010.20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Explaining Classification by Finding Response-Related Subgroups in Data
A method for explaining results of a regression based classifier is proposed. The data is clustered using a metric extracted from the classifier. This way, clusters found are related to classifier predictions, and each cluster can be considered a possible explanation for classification result. The clusters are described by simple rules, meant to be easy for a human to understand. The key points of the work are presenting a modular framework for explaining the classification, and studying and comparing two different approaches for extracting a metric from a classifier model.