TY - GEN
T1 - Active learning from relative queries
AU - Qian, Buyue
AU - Wang, Xiang
AU - Wang, Fei
AU - Li, Hongfei
AU - Ye, Jieping
AU - Davidson, Ian
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84896061426&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896061426&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84896061426
SN - 9781577356332
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1614
EP - 1620
BT - IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
T2 - 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
Y2 - 3 August 2013 through 9 August 2013
ER -