Tackling outliers in granular box regression

Journal article

Peters, Georg and Lacic, Zdravko. (2012). Tackling outliers in granular box regression. Information Sciences. 212, pp. 44 - 56. https://doi.org/10.1016/j.ins.2012.05.006
AuthorsPeters, Georg and Lacic, Zdravko
Abstract

Granular computing has gained increasing attention in the last decade. It is motivated by the needs for simply and robust low cost solutions in many real life applications. Addressing these needs, the main objective of granular computing is to develop efficient algorithms. Today granular computing provides a rich variety of such algorithms including methods derived from interval mathematics, fuzzy and rough sets and others. Within this framework granular box regression was proposed recently. Granular box regression uses hyper-dimensional interval numbers to establish a f.g-generalization of a function between several independent variables and one dependent variable. Since granular box regression utilizes intervals a challenge is the detection of outliers. In this paper, we propose three methods tackling outliers in granular box regression and discuss their properties. We also apply these methods to artificial as well as to real data.

Keywordsgranular computing; granular box regression; outliers
Year2012
JournalInformation Sciences
Journal citation212, pp. 44 - 56
PublisherElsevier B.V.
ISSN0020-0255
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ins.2012.05.006
Scopus EID2-s2.0-84862977873
Page range44 - 56
Research GroupSchool of Arts
Publisher's version
File Access Level
Controlled
Place of publicationNetherlands
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