Mixed Aggregation Functions for Outliers Detection

Hengshan Zhang, Chunru Chen, Tianhua Chen, Zhongmin Wang, Yanping Chen

Research output: Contribution to journalArticlepeer-review

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 nonuniform 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.
Original languageEnglish
JournalJournal of Intelligent and Fuzzy Systems
Publication statusAccepted/In press - 14 Dec 2020

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