A Fuzzy Set/Rule Distance for Evolving Fuzzy Anomaly Detectors

Author

Gómez, Jonatan ; León, Elizabeth

Author_Institution

Univ. Nacional de Colombia, Bogota

fYear

0

fDate

0-0 0

Firstpage

2286

Lastpage

2292

Abstract

This paper develops a notion of quasi distance between fuzzy sets (rule detectors) that preserves a notion of fuzzy set (rule) dominance. Such quasi distance notion is used by an evolutionary algorithm during its deterministic crowding mechanism in order to preserve diversity of the evolved fuzzy rule detectors. Moreover, the evolutionary process uses a variable length encoding that allows to tune up the fuzzy set associated to each attribute in the atomic condition of a fuzzy rule by evolving the fuzzy set parameters. Experiments with real data sets are performed and some results are reported.

Keywords

evolutionary computation; fuzzy reasoning; fuzzy set theory; learning (artificial intelligence); pattern classification; security of data; variable length codes; anomaly classification; atomic condition; deterministic crowding mechanism; evolutionary algorithm; fuzzy anomaly detection; fuzzy rule detector; fuzzy set theory; quasi distance notion; supervised learning; variable length encoding; Character generation; Detectors; Encoding; Evolutionary computation; Fuzzy sets; Genetic algorithms; Humans; Immune system; Shape; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2006 IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9488-7

Type

conf

DOI

10.1109/FUZZY.2006.1682017

Filename

1682017