Improving Multiclass Classification in Crowdsourcing by Using Hierarchical Schemes

Xiaoni Duan, Keishi Tajima

Abstract: In this paper, we propose a method of improving accuracy of multiclass classification tasks in crowdsourcing. In crowdsourcing, it is important to assign appropriate workers to appropriate tasks. In multiclass classification, different workers are good at different subcategories. In our method, we reorganize a given flat classification task into a hierarchical classification task consisting of several subtasks, and assign each worker to an appropriate subtask. In this approach, it is important to choose a good hierarchy. In our method, we first post a flat classification task with a part of data and collect statistics on each worker's ability. Based on the obtained statistics, we simulate all candidate hierarchical schemes, estimate their expected accuracy, choose the best scheme, and post it with the rest of data. In our method, it is also important to allocate workers to appropriate subtasks. We designed several greedy worker allocation algorithms. The results of our experiments show that our method improves the accuracy of multiclass classification tasks.