Classification-oriented dawid skene model for transferring intelligence from crowds to machines  

作　　者：Jiaran LI Richong ZHANG Samuel MENSAH Wenyi QIN Chunming HU 

机构地区：[1]Department of Computer Science and Engineering,Beihang University,Beijing 100191,China [2]Department of Computer Science,University of Sheffield,Sheffield,S102TN,UK

出　　处：《Frontiers of Computer Science》2023年第5期53-66,共14页中国计算机科学前沿（英文版）

基　　金：supported in part by the National Key R&D Program of China(2021ZD0110700);in part by the Fundamental Research Funds for the Central Universities,in part by the State Key Laboratory of Software Development Environment;in part by a Leverhulme Trust Research Project Grant.

摘　　要：When a crowdsourcing approach is used to assist the classification of a set of items,the main objective is to classify this set of items by aggregating the worker-provided labels.A secondary objective is to assess the workers’skill levels in this process.A classical model that achieves both objectives is the famous Dawid-Skene model.In this paper,we consider a third objective in this context,namely,to learn a classifier that is capable of labelling future items without further assistance of crowd workers.By extending the DawidSkene model to include the item features into consideration,we develop a Classification-Oriented Dawid Skene(CODS)model,which achieves the three objectives simultaneously.The effectiveness of CODS on this three dimensions of the problem space is demonstrated experimentally.

关 键 词：crowdsourcing information aggregation lear-ning from crowds 

分 类 号：O15[理学—数学]