Proceedings

Active Learning from Relative Queries

Buyue Qian, Xiang Wang, Fei Wang, Hongfei Li, Jieping Ye, Ian Davidson

Download BibTeX Cite

Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1614-1619.

Abstract

Active learning has been extensively studied and shown to be useful in solving real problems. The typical setting of traditional active learning methods is querying labels from an oracle. This is only possible if an expert exists, which may not be the case in many real world applications. In this paper, we focus on designing easier questions that can be answered by a non-expert. These questions poll relative information as opposed to absolute information and can be even generated from side-information. We propose an active learning approach that queries the ordering of the importance of an instance's neighbors rather than its label. We explore our approach on real datasets and make several interesting discoveries including that querying neighborhood information can be an effective question to ask and sometimes can even yield better performance than querying labels.

Buyue Qian, Xiang Wang, Fei Wang, Hongfei Li, Jieping Ye, and Ian Davidson. 2013. Active Learning from Relative Queries. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 1614–1619. https://ijcai.org/proceedings/2013/240
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards