Affiliations: [a] The School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China
| [b] The Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| [c] The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author. Chunru Chen, The School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China. E-mail: [email protected].
Abstract: A scenario that often encounters in the event of aggregating options of different experts for the acquisition of a robust overall consensus is the possible existence of extremely large or small values termed as outliers in this paper, which easily lead to counter-intuitive results in decision aggregation. This paper attempts to devise a novel approach to tackle the consensus outliers especially for non-uniform data, filling the gap in the existing literature. In particular, the concentrate region for a set of non-uniform data is first computed with the proposed searching algorithm such that the domain of aggregation function is partitioned into sub-regions. The aggregation will then operate adaptively with respect to the corresponding sub-regions previously partitioned. Finally, the overall aggregation is operated with a proposed novel consensus measure. To demonstrate the working and efficacy of the proposed approach, several illustrative examples are given in comparison to a number of alternative aggregation functions, with the results achieved being more intuitive and of higher consensus.
