Burcu Sayin, E. Krivosheev, Jorge Ramírez, F. Casati, E. Taran, V. Malanina, Jie Yang
{"title":"Crowd-Powered Hybrid Classification Services: Calibration is all you need","authors":"Burcu Sayin, E. Krivosheev, Jorge Ramírez, F. Casati, E. Taran, V. Malanina, Jie Yang","doi":"10.1109/ICWS53863.2021.00019","DOIUrl":null,"url":null,"abstract":"Hybrid classification services are online services that combine machine learning (ML) and humans - either crowd workers or experts - to achieve a classification objective, from relatively simple ones such as deriving the sentiment of a text to more complex ones such as medical diagnoses. This paper takes the first steps toward a science for hybrid classification services, discussing key concepts, challenges, and architectures, and then focusing on a central aspect, that of ML calibration and how it can be achieved with crowdsourced labels.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Web Services (ICWS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWS53863.2021.00019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Hybrid classification services are online services that combine machine learning (ML) and humans - either crowd workers or experts - to achieve a classification objective, from relatively simple ones such as deriving the sentiment of a text to more complex ones such as medical diagnoses. This paper takes the first steps toward a science for hybrid classification services, discussing key concepts, challenges, and architectures, and then focusing on a central aspect, that of ML calibration and how it can be achieved with crowdsourced labels.