Reliability-guided fuzzy classifier ensemble

Tianhua Chen, Pan Su, Changjing Shang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


Classifier ensembles form an important approach to improving classification performance. As such, there have been different proposals made in the literature that provide a range of means to construct and aggregate classifier ensembles. However, the resulting systems may contain unreliable members with false or biased judgements in the ensemble. The removal of unreliable members is necessary to optimise the overall performance of such systems. Smaller ensembles involving reduced ensemble members also helps relax the requirement of computational memory, thereby strengthening the system's run-time efficiency. To differentiate the potential contributions of different ensemble members while reducing the adverse impact of any unreliable judgement upon the system, a nearest neighbour-based reliability measure is incorporated into the process of classifier ensemble selection. In particular, reliabilities of selected ensemble members are perceived as a stress function, from which argument-dependent weights are heuristically generated for final aggregated decision. Experimental investigations are carried out, demonstrating the efficacy of the proposed approach, where fuzzy classifiers are utilised as base members of the emerging ensemble.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN (Electronic)9781509060344
ISBN (Print)9781509060351
Publication statusPublished - 24 Aug 2017
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems 2017 - Royal Continental Hotel, Naples, Italy
Duration: 9 Jul 201712 Jul 2017 (Link to Conference Website)


ConferenceIEEE International Conference on Fuzzy Systems 2017
Abbreviated titleFUZZ-IEEE 2017
Internet address


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