A comprehensive benchmark of the artificial immune recognition system (AIRS)

Authors:
Lingjun Meng;Peter van der Putten;Haiyang Wang
Affiliations:
Network Center, Shandong University, P.R. China;Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands;Network Center, Shandong University, P.R. China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

Citing 5
Cited 3

Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

Genetic Programming and Evolvable Machines
A Bias-Variance Analysis of a Real World Learning Problem: The CoIL Challenge 2000

Machine Learning

2009 Special Issue: A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept

Neural Networks
On AIRS and Clonal Selection for Machine Learning

ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Improving the accuracy of AIRS by incorporating real world tournament selection in resource competition phase

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation

Quantified Score

Hi-index	0.02

Visualization

Abstract

Artificial Immune Systems are a new class of algorithms inspired by how the immune system recognizes, attacks and remembers intruders. This is a fascinating idea, but to be accepted for mainstream data mining applications, extensive benchmarking is needed to demonstrate the reliability and accuracy of these algorithms. In our research we focus on the AIRS classification algorithm. It has been claimed previously that AIRS consistently outperforms other algorithms. However, in these papers AIRS was compared to benchmark results from literature. To ensure consistent conditions we carried out benchmark tests on all algorithms using exactly the same set up. Our findings show that AIRS is a stable and robust classifier that produces around average results. This contrasts with earlier claims but shows AIRS is mature enough to be used for mainstream data mining.