{"type":"ScholarlyArticle","title":"Active Learning with Multi-Label SVM Classification","authors":[{"name":"Xin Li","slug":"xin-li-2","given":"Xin","family":"Li","affiliation":""},{"name":"Yuhong Guo","slug":"yuhong-guo","given":"Yuhong","family":"Guo","affiliation":""}],"abstract":"Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more time-consuming or expensive to obtain than annotations of single-label instances. Though active learning has been widely studied on reducing labeling effort for single-label problems, current research on multi-label active learning remains in a preliminary state. In this paper, we first propose two novel multi-label active learning strategies, a max-margin prediction uncertainty strategy and a label cardinality inconsistency strategy, and then integrate them into an adaptive framework of multi-label active learning. Our empirical results on multiple multi-label data sets demonstrate the efficacy of the proposed active instance selection strategies and the integrated active learning approach.","doi":null,"url":"https://ijcai.org/proceedings/2013/221","pdf":"https://ijcai.org/proceedings/2013/0221.pdf","markdown":"https://ijcai.org/proceedings/2013/221.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/221","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":221,"pages":{"first":1479,"last":1484},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}