Attribute augmentation-based label integration for crowdsourcing

作     者：Yao ZHANG Liangxiao JIANG Chaoqun LI Yao ZHANG;Liangxiao JIANG;Chaoqun LI

作者机构：School of Computer ScienceChina University of GeosciencesWuhan 430074China School of Mathematics and PhysicsChina University of GeosciencesWuhan 430074China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2023年第17卷第5期

页      面：41-51页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Science and Technology Project of Hubei Province-Unveiling System(2021BEC007) the Industry-University-Research Innovation Funds for Chinese Universities(2020ITA05008) 

主　　题：crowdsourcing label integration attribute aug-mentation instance filtering 

摘      要：Crowdsourcing provides an effective and low-cost way to collect labels from crowd *** to the lack of professional knowledge,the quality of crowdsourced labels is relatively low.A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true ***,to our knowledge,almost all existing label integration methods merely make use of the original attribute information and do not pay attention to the quality of the multiple noisy label set of each *** solve these issues,this paper proposes a novel three-stage label integration method called attribute augmentation-based label integration(AALI).In the first stage,we design an attribute augmentation method to enrich the original attribute *** the second stage,we develop a filter to single out reliable instances with high-quality multiple noisy label *** the third stage,we use majority voting to initialize integrated labels of reliable instances and then use cross-validation to build multiple component classifiers on reliable instances to predict all *** results on simulated and real-world crowdsourced datasets demonstrate that AALI outperforms all the other stateof-the-art competitors.