Fuzzy-Rough-Set-Based Active Learning

Ran WANG, Degang CHEN, Sam KWONG

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

33 Citations (Scopus)


Determining the informativeness of unlabeled samples is a key issue in active learning. One solution to this is using the sample's inconsistency between conditional features and decision labels. In this paper, a fuzzy-rough-set-based active learning model is proposed to tackle this problem. First, the consistence degree of a labeled sample is computed by the lower approximations in fuzzy rough set, which reflects its minimum membership in the decision class. Then, the concept of sample covering is proposed to measure the relationship between labeled samples and unlabeled samples. Afterward, the memberships of an unlabeled sample belonging to different decision classes are computed based on the covering degrees of labeled samples on it. Finally, these memberships are used to form a sample selection criterion to measure the sample's inconsistency. By applying Gaussian kernel-based similarity relation to the aforementioned processes, a support vector machine (SVM)-based active learning scheme is developed. Experimental results demonstrate the effectiveness of the proposed model.

Original languageEnglish
Article number6670768
Pages (from-to)1699-1704
Number of pages6
JournalIEEE Transactions on Fuzzy Systems
Issue number6
Early online date20 Nov 2013
Publication statusPublished - Dec 2014
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant 71171080 and Grant 61272289.

Publisher Copyright:
© 1993-2012 IEEE.


  • Active learning
  • fuzzy rough set
  • inconsistency
  • sample covering
  • support vector machine (SVM)


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