Refinement of weights using attribute support for multiple attribute decision making

Hengshan Zhang, Yimin Zhou, Tianhua Chen, Richard Hill, Zhongmin Wang, Yanping Chen

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


A number of approaches have been proposed to determine the weights for multiple attribute decision making. However, the resultant weights are usually assumed to be fixed, making it lack of tolerance to accommodate variation if the patterns of the subsequent data are subject to change. This article proposes a method to facilitate the adjustment of attribute weights, which accommodates a number of relevant characteristics. A model is first constructed that is able to express the requirements of a particular application. The concept of attribute support and consensus are then proposed for subsequent weight modification. A full algorithm is finally presented for the attribute weight adjustment. The effectiveness of the proposed method is validated by way of a case study in the tax credit domain with a sensitivity analysis of the method further evaluated.
Original languageEnglish
Article number101440
Pages (from-to)26-39
Number of pages14
JournalJournal of Computational Science
Early online date8 Sep 2021
Publication statusPublished - 8 Sep 2021


Dive into the research topics of 'Refinement of weights using attribute support for multiple attribute decision making'. Together they form a unique fingerprint.

Cite this