MILA - Multilevel immune learning algorithm and its application to anomaly detection
Abstract
T-cell-dependent humoral immune response is one of the more complex immunological events in the biological immune system, involving interaction of B cells with antigen (Ag) and their proliferation, differentiation and subsequent secretion of antibody (Ab). Inspired by these immunological principles, a Multilevel Immune Learning Algorithm (MILA) is proposed for novel pattern recognition. This paper describes the detailed background of MILA, and outlines its main features in different phases: Initialization phase, Recognition phase, Evolutionary phase and Response phase. Different test problems are studied and experimented with MILA for performance evaluation. The results show MILA is flexible and efficient in detecting anomalies and novel patterns. © Springer-Verlag 2003.
Publication Title
Soft Computing
Recommended Citation
Dasgupta, D., Yu, S., & Majumdar, N. (2005). MILA - Multilevel immune learning algorithm and its application to anomaly detection. Soft Computing, 9 (3), 172-184. https://doi.org/10.1007/s00500-003-0342-7
