Paper Info
Title
MAICS: multilevel artificial immune classification system
Abstract
This paper presents a novel approach to feature selection and multiple-class classification problems. The proposed method is based on metaphors derived from artificial immune systems, clonal and negative selection paradigms. A novel clonal selection algorithm – Immune K-Means, is proposed. The proposed system is able to perform feature selection and model identification tasks by evolving specialized subpopulations of T- and B-lymphocytes. Multilevel evolution and real-valued coding enable for further extending of the proposed model and interpreting the subpopulations of lymphocytes as sets of evolving fuzzy rules.
Year
DOI
Venue
2006
10.1007/11785231_59
ICAISC
Keywords
Field
DocType
feature selection,specialized subpopulations,novel approach,model identification task,negative selection paradigm,immune k-means,novel clonal selection algorithm,proposed system,multilevel artificial immune classification,model identification,artificial immune system,classification system,negative selection,k means
Artificial immune system,Negative selection,Feature selection,Computer science,Fuzzy set,Artificial intelligence,Soft computing,Clonal selection algorithm,Clonal selection,Machine learning,Fuzzy rule
Conference
Volume
ISSN
ISBN
4029
0302-9743
3-540-35748-3
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
Michal Bereta110.70
Tadeusz Burczynski2336.69