Granular box regression

Journal article


Peters, Georg. (2011) Granular box regression. IEEE Transactions on Fuzzy Systems. 19(6), pp. 1141 - 1152. https://doi.org/10.1109/TFUZZ.2011.2162416
AuthorsPeters, Georg
Abstract

Granular computing (GrC) has gained increasing attention in the past decade. Although not uniquely defined, its basic idea is to approximate detailed machine-like information by a coarser presentation on a human-like level. Within granular computing, the mapping of continuous variables into intervals plays an important role. These intervals are often prerequisites for the formulation of linguistic variables. In this paper, we suggest a piecewise interval approximation and propose granular box regression. Its objective is to establish relationships between independent and dependent variables by multidimensional boxes. We interpret granular box regression as interval regression and show its potential for the extraction of fuzzy rules from data. In two experiments, we apply granular box regression to an artificial as well as to a real dataset in the field of finance and evaluate its properties.

Keywordsapproximation methods; clustering algorithms; minimization; regression analysis
Year2011
JournalIEEE Transactions on Fuzzy Systems
Journal citation19 (6), pp. 1141 - 1152
PublisherInstitute of Electrical and Electronics Engineers
ISSN1063-6706
Digital Object Identifier (DOI)https://doi.org/10.1109/TFUZZ.2011.2162416
Scopus EID2-s2.0-82455164453
Page range1141 - 1152
Research GroupSchool of Arts
Place of publicationUnited States
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