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.

研究了用传统机器学习技术代替 Dendritic Cell Algorithm 分类阶段的效果以及该算法的动态过滤功能对异常检测问题的实用性，并发现 DCA 仍具有待研究的独特特性和潜在应用，建议在人工免疫系统社区进一步探讨其应用领域。

课堂安静：分类、噪声及树突状细胞算法

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

The Dendritic Cell Algorithm is an immune-inspired algorithm orig- inally
based on the function of natural dendritic cells. The original instantiation of
the algorithm is a highly stochastic algorithm. While the performance of the
algorithm is good when applied to large real-time datasets, it is difficult to
anal- yse due to the number of random-based elements. In this paper a
deterministic version of the algorithm is proposed, implemented and tested
using a port scan dataset to provide a controllable system. This version
consists of a controllable amount of parameters, which are experimented with in
this paper. In addition the effects are examined of the use of time windows and
variation on the number of cells, both which are shown to influence the
algorithm. Finally a novel metric for the assessment of the algorithms output
is introduced and proves to be a more sensitive metric than the metric used
with the original Dendritic Cell Algorithm.

本文提出了一种 Dendritic Cell Algorithm 的确定性版本，用端口扫描数据集进行实验，发现时间窗口和细胞数量的变化有影响，引入了一种新的评估算法输出的度量方法，比原来的算法更敏感。