An Agent-based Classification Model

Gu, Feng; Aickelin, Uwe; Greensmith, Julie

Abstract

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal
and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer
systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the
feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes
it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.

Citation

Gu, F., Aickelin, U., & Greensmith, J. An Agent-based Classification Model. Presented at The 9th European Agent Systems Summer School (EASSS 2007)

• Conference Name: The 9th European Agent Systems Summer School (EASSS 2007)
• Deposit Date: Oct 12, 2007
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1017437