Immunology as a Metaphor for Computational ... - Napier University

1 £ b)-1(c) simulate the metadynamics of the immune network. Note that steps 1 £ a¥££££Chapter 2. Background 32supervision could not correctly classify these items. In order to separate these items,some warping of the dimensions must be undertaken, which cannot be performed withoutsupervision. No information could be found in the literature as to the percentage ofitems in the Fisher set correctly classified by the AINE algorithm, therefore despite theappearance of three distinct clusters within the data in the diagrams given for examplein [Timmis and Neal, 2001], it would be interesting to compare the number of itemscorrectly classified to that produced by other more established methods.The second influential network model is a system named aiNet, due toDe Castro and Von Zuben, and described in [De Castro and Von Zuben, 2000b,De Castro and Von Zuben, 2001]. The system was designed with the goals of dataclustering and of filtering redundant data. In this model, the immune network is consideredas an edge-weighted graph, not necessarily fully connected, composed of aset of nodes (cells) and node pairs (edges) which are assigned a weight or connectionstrength. The network is evolutionary in the sense that evolution strategies areused to control the network dynamics and plasticity, and also connectionist once a matrixof connection strengths is defined to measure the affinities between the networkcells. As in the AINE model described above, network cells compete for antigenicrecognition, and those successful undergo cell proliferation, whilst antibody-antibodyrecognition results in network suppression. Similarity in this system is also calculatedon the basis of Euclidean distance between cells. The exact algorithm is given in figure2.8 — steps (1 a¥ i-1 £ a¥ vii) simulate the clonal selection and affinity maturation£processes occurring in the natural immune system, steps 1 £ a¥x¥ and stepsand 1 £ a¥x¥ introduce both clonal suppression and network suppression elements toviii¥ -1 £ a¥the algorithm. The model contains rather a large number of parameters, and also has ahigh computational cost per iteration (of the order O p 3 ¥ ). Furthermore, it is difficultto determine sensible stopping criteria.£The network outputs consist of a matrix of memory cell coordinates and a matrixof inter-cell affinities. The network structure is analysed by calculating the minimumspanning tree of the network — this allows the clusters in the data to be identified andalso a means of determining which network cell belongs to which cluster. Results areix¥