Artificial immune systems : a novel data analysis technique inspired by the immune network theory
Artificial immune systems : a novel data analysis technique inspired by the immune network theory
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发表时间：
2000-08
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通讯作者：
J. Timmis
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作者：
J. Timmis
本文提出了一种受自然免疫系统启发的新的数据分析技术。免疫隐喻被提取、简化并应用于创建有效的数据分析技术。本论文在前人工作的基础上，提取免疫系统的显著特征，建立了一种原则性的、有效的数据分析技术。在整个论文中，采用了一种有条不紊和原则性的方法。以前的工作，沿着背景免疫学进行了广泛的调查。确定了之前研究中的问题，并提取了免疫学原理来创建用于数据分析的初始AIS。通过克隆和突变过程，AIS建立了一个B细胞网络，这些细胞是正在分析的数据的多样化代表。该网络通过专门开发的工具可视化。这允许用户与网络交互并使用系统进行探索性数据分析。在两个不同的数据集上进行实验，一个简单的模拟数据集和Fisher Iris数据集。AIS在这两个集合上都获得了良好的结果，AIS能够识别已知存在于其中的集群。算法的行为进行了广泛的调查，并在算法参数影响性能和结果的方式也进行了检查。尽管最初的AIS取得了成功，但发现了算法的问题，并进行了第二阶段的研究。这导致了资源有限的人工免疫系统（RLAIS），它创建了一个稳定的对象网络，一旦发现，这些对象不会恶化或丢失模式。稳定的网络规模的时期，观察到的网络规模的扰动。本论文提出了一个成功的应用免疫系统隐喻，创造一个新的数据分析技术。此外，RLAIS在使AIS成为有效数据分析的可行竞争者方面还有很长的路要走，并确定了进一步的研究。
This thesis presents a novel data analysis technique inspired by the natural immune system. Immunological metaphors were extracted, simplified and applied to create an effective data analysis technique. This thesis builds on foundations of previous work, extracts salient features of the immune system and creates a principled and effective data analysis technique. Throughout this thesis, a methodical and principled approach was adopted. Previous work, along with background immunology was extensively surveyed. Problems with previous research were identified and principles from immunology were extracted to create the initial AIS for data analysis. The AIS, through the process of cloning and mutation, built up a network of B cells that were a diverse representation of data being analysed. This network was visualised via a specially developed tool. This allows the user to interact with the network and use the system for exploratory data analysis. Experiments were performed on two different data sets, a simple simulated data set and the Fisher Iris data set. Good results were obtained by the AIS on both sets, with the AIS being able to identify clusters known to exist within them. Extensive investigation into the algorithm's behaviour was undertaken and the way in which algorithm parameters effected performance and results was also examined. Despite initial success from the original AIS, problems were identified with the algorithm and the second stage of research was undertaken. This resulted in the resource limited artificial immune system (RLAIS) which created a stable network of objects that did not deteriorate or loose patterns once discovered. Periods of stable network size were observed with perturbations of the network size. This thesis presents a successful application of immune system metaphors to create a novel data analysis technique. Furthermore, the RLAIS goes a long way toward making AIS a viable contender for effective data analysis and further research is identified for study.