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.
|Title of host publication||2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)|
|Publication status||Published - 24 Aug 2017|
|Event||IEEE International Conference on Fuzzy Systems - Royal Continental Hotel, Naples, Italy|
Duration: 9 Jul 2017 → 12 Jul 2017
http://www.fuzzieee2017.org/ (Link to Conference Website)
|Conference||IEEE International Conference on Fuzzy Systems|
|Abbreviated title||FUZZ-IEEE 2017|
|Period||9/07/17 → 12/07/17|
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- Department of Computer Science - Senior Lecturer in Artificial Intelligence
- School of Computing and Engineering
- Centre for Planning, Autonomy and Representation of Knowledge - Member
- Centre of Artificial Intelligence for Mental Health