Title :
A Fuzzy Set/Rule Distance for Evolving Fuzzy Anomaly Detectors
Author :
Gómez, Jonatan ; León, Elizabeth
Author_Institution :
Univ. Nacional de Colombia, Bogota
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;
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
DOI :
10.1109/FUZZY.2006.1682017
