Ambiguity-based multiclass active learning


Research output: Journal PublicationsJournal Article (refereed)peer-review

31 Citations (Scopus)


Most existing works on active learning (AL) focus on binary classification problems, which limit their applications in various real-world scenarios. One solution to multiclass AL (MAL) is evaluating the informativeness of unlabeled samples by an uncertainty model and selecting the most uncertain one for query. In this paper, an ambiguity-based strategy is proposed to tackle this problem by applying a possibility approach. First, the possibilistic memberships of unlabeled samples in the multiple classes are calculated from the one-against-all-based support vector machine model. Then, by employing fuzzy logic operators, these memberships are aggregated into a new concept named k-order ambiguity, which estimates the risk of labeling a sample among k classes. Afterward, the k-order ambiguities are used to form an overall ambiguity measure to evaluate the uncertainty of the unlabeled samples. Finally, the sample with the maximum ambiguity is selected for query, and a new MAL strategy is developed. Experiments demonstrate the feasibility and effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)242-248
JournalIEEE Transactions on Fuzzy Systems
Issue number1
Early online date1 Jul 2015
Publication statusPublished - Feb 2016
Externally publishedYes

Bibliographical note

This work was supported in part by Hong Kong RGC General Research Fund GRF Grant 9042038 (CityU 11205314), the National Natural Science Foundation of China (Grant 61402460 and Grant 61472257), the Guangdong Provincial Science and Technology Plan Project (No. 2013B040403005), and the HD Video R&D Platform for Intelligent Analysis and Processing in Guangdong Engineering Technology Research Centre of Colleges and Universities (No. GCZX-A1409).


  • Active learning
  • ambiguity
  • Fuzzy sets and fuzzy logic


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