Classification based on upper integral

Author

Chen, Ai-xia ; Liang, Zhi-yong ; Feng, Hui-min

Author_Institution

Dept. of Math. & Comput. Sci., Hebei Univ., Baoding, China

Volume

2

fYear

2011

fDate

10-13 July 2011

Firstpage

835

Lastpage

840

Abstract

The upper integral is a type of non-linear integral with respect to non-additive measures, which represents the maximum potential of efficiency for a group of features with interaction. The value of upper integrals can be evaluated through solving a linear programming problem. Considering the upper integral as a classifier, this paper investigates its implementation and performance. The difficult step in the implementation is how to learn the non-additive set function used in upper integrals. Numerical simulations on some benchmark data sets are given.

Keywords

fuzzy set theory; linear programming; numerical analysis; pattern classification; linear programming problem; nonadditive set function; nonlinear integral; numerical simulation; upper integral classifier; Cybernetics; Genetic algorithms; Histograms; Interpolation; Machine learning; Possibility theory; Weight measurement; Fuzzy integral; Fuzzy measure; Genetic algorithm; Multi-attribute classification; Possibility distribution; Upper integral;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016828

Filename

6016828