{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:08Z","timestamp":1723016048264},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Learning of classification models in practice often relies on nontrivial human annotation effort in which humans assign class labels to data instances. As this process can be very time consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. In this work we solve this problem by exploring a new approach that actively learns classification models from groups, which are subpopulations of instances, and human feedback on the groups. Each group is labeled with a number in [0,1] interval representing a human estimate of the proportion of instances with one of the class labels in this subpopulation. To form the groups to be annotated, we develop a hierarchical active learning framework that divides the whole population into smaller subpopulations, which allows us to gradually learn more refined models from the subpopulations and their class proportion labels. Our extensive experiments on numerous datasets show that our method is competitive and outperforms existing approaches for reducing the human annotation cost.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/351","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"2532-2538","source":"Crossref","is-referenced-by-count":5,"title":["Hierarchical Active Learning with Group Proportion Feedback"],"prefix":"10.24963","author":[{"given":"Zhipeng","family":"Luo","sequence":"first","affiliation":[{"name":"Computer Science Department, University of Pittsburgh, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Milos","family":"Hauskrecht","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Pittsburgh, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:51:52Z","timestamp":1530769912000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/351"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/351","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}