NDLI: A modified algorithm to find a representative capacity with evenness consideration for non-additive robust ordinal regression

Content Provider	IEEE Xplore Digital Library
Author	Hemmatjou, R. Nahavandi, N. Moshiri, B. Kamalabadi, I.N.
Copyright Year	2011
Description	Author affiliation: CIPCE, School of Electrical & Computer Engineering, Tehran University, Iran (Moshiri, B.) \|\| Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran (Hemmatjou, R.; Nahavandi, N.; Kamalabadi, I.N.)
Abstract	Non-additive robust ordinal regression (NAROR), uses primary preferences of decision maker (DM) to define the necessary preference relations (NPR) and possible preference relations (PPR) on alternatives when all compatible fuzzy measures are taken into account and aggregation function is Choquet integral (CI). The question arises as how these NPRs and PPRs can be used in capacity definition problem? This article proposes an algorithm which uses these relations in finding a capacity that is representative to DM and has also evenness property to some extent. The methods based on maximizing evenness leads to results that are not fully representative to DM, so this article improves this drawback by focusing on representativeness of capacity and taking into account capacity evenness by means of decision rules defined in algorithm.
Starting Page	21
Ending Page	25
File Size	689190
Page Count	5
File Format	PDF
ISBN	9781457707407
ISSN	2157362X
e-ISBN	9781457707391
e-ISBN	9781457707384
DOI	10.1109/IEEM.2011.6117871
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Additives Choquet integral necessary preference relation possible preference relation non-additive robust ordinal regression Europe Educational institutions Linear programming Delta modulation Robustness evenness of capacity Indexes
Content Type	Text
Resource Type	Article

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