Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm

TL;DR

This paper critically evaluates the Dendritic Cell Algorithm's unique features, replacing its classification stage with traditional methods, and finds that some properties may not offer advantages over conventional approaches for anomaly detection.

Contribution

It investigates the effects of replacing the DCA's classification stage with traditional machine learning techniques and questions the utility of its dynamic filtering property.

Findings

Dynamic filtering is less effective than static filtering for synthetic data.

Replacing DCA's classification with traditional methods can improve performance.

Some unique DCA features may not provide benefits over standard approaches.

Abstract

Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes have been suggested in the literature to correct for these findings. A contribution of this work is to investigate the effects of replacing the classification stage of the DCA (which is known to be flawed) with a traditional machine learning technique. This work goes on to question the merits of those unique properties of the DCA that are yet to be thoroughly analysed. If none of these properties can be found to have a benefit over traditional approaches, then "fixing" the DCA is arguably less efficient than simply creating a new algorithm. This work examines the dynamic filtering property of the DCA and questions the utility of this unique feature for the anomaly detection problem. It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset. It is concluded that there are still unique features of the DCA left to investigate. Areas that may be of benefit to the Artificial Immune Systems community are suggested.