Supervised Learning to Aggregate Data With the Sugeno Integral

Marek Gągolewski , Simon James , Gleb Beliakov


The problem of learning symmetric capacities (or fuzzy measures) from data is investigated toward applications in data analysis and prediction as well as decision making. Theoretical results regarding the solution minimizing the mean absolute error are exploited to develop an exact branch-refine-and-bound-type algorithm for fitting Sugeno integrals (weighted lattice polynomial functions, max-min operators) with respect to symmetric capacities. The proposed method turns out to be particularly suitable for acting on ordinal data. In addition to providing a model that can be used for the general data regression task, the results can be used, among others, to calibrate generalized h-indices to bibliometric data.
Author Marek Gągolewski (FMIS / DIE) - [Systems Research Institute (IBS PAN) [Polska Akademia Nauk (PAN)]]
Marek Gągolewski,,
- Department of Integral Equations
- Instytucie Badań Systemowych
, Simon James
Simon James,,
, Gleb Beliakov
Gleb Beliakov,,
Journal seriesIEEE Transactions on Fuzzy Systems, ISSN 1063-6706, (A 50 pkt)
Issue year2019
Publication size in sheets0.5
Keywords in EnglishFuzzy measures , h-index , lattice polynomials , ordinal data fitting , Sugeno integral , weight learning
ASJC Classification2604 Applied Mathematics; 1702 Artificial Intelligence; 1703 Computational Theory and Mathematics; 2207 Control and Systems Engineering
Languageen angielski
Score (nominal)50
ScoreMinisterial score = 50.0, 15-05-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 3.29; WoS Impact Factor: 2017 = 8.415 (2) - 2017=9.34 (5)
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