Active learning from relative queries

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages1614-1620
Number of pages7
Publication statusPublished - 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period8/3/138/9/13

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Qian, B., Wang, X., Wang, F., Li, H., Ye, J., & Davidson, I. (2013). Active learning from relative queries. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1614-1620)