A Comprehensive Benchmark of the Artificial Immune Recognition System (AIRS)

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.