AiNet System
An AiNet System is an Artificial Immune System Network that implements AiNet Algorithms to solve a AiNet Task.
- AKA: \aiNet.
- Context:
- It also employs the clonal selection and affinity maturation algorihtms.
- …
- Example(s):
- Counter-Example(s):
- See: Immune Network, Artificial Immune Network Topology, Neural Network, Machine Learning System, Clonal Selection System.
References
2002
- (de Castro & Von Zuben, 2002) ⇒ Leandro Nunes de Castro & Fernando José Von Zuben (2002). "aiNet: an artificial immune network for data analysis" (PDF). In: Hussein A. Abbass, Ruhul A. Sarker and Charles S. Newton (Eds). Data mining: a heuristic approach (PDF) (pp. 231-260). IGI Global. ISBN 1-930708-25-4
- QUOTE: To develop our artificial immune network model, named aiNet, we will employ the immune network theory, and the clonal selection and affinity maturation principles. In summary, the immune network theory hypothesizes the activities of the immune cells, the emergence of memory and the discrimination between our own cells (known as self) and external invaders (known as nonself). It also suggests that the immune system has an internal image of all pathogens (infectious nonself) to which it was exposed during its lifetime. On the other hand, the clonal selection principle proposes a description of the way the immune system copes with the pathogens to mount an adaptive immune response. The affinity maturation principle is used to explain how the immune system becomes increasingly better at its task of recognizing and eliminating these pathogens (antigenic substances).
The aiNet model will consist of a set of cells, named antibodies, interconnected by links with associated connection strengths. The aiNet antibodies are supposed to represent the network internal images of the pathogens (input patterns) to which they are exposed. The connections between the antibodies will determine their interrelations, providing a degree of similarity (in a given metric space) among them: the closer the antibodies, the more similar they are. (...) The proposed artificial immune network model can be formally defined:
*** Definition 1: The aiNet is an edge-weighted graph, not necessarily fully connected, composed of a set of nodes, called antibodies, and sets of node pairs called edges with an assigned number called weight, or connection strength, associated with each connected edge.
- QUOTE: To develop our artificial immune network model, named aiNet, we will employ the immune network theory, and the clonal selection and affinity maturation principles. In summary, the immune network theory hypothesizes the activities of the immune cells, the emergence of memory and the discrimination between our own cells (known as self) and external invaders (known as nonself). It also suggests that the immune system has an internal image of all pathogens (infectious nonself) to which it was exposed during its lifetime. On the other hand, the clonal selection principle proposes a description of the way the immune system copes with the pathogens to mount an adaptive immune response. The affinity maturation principle is used to explain how the immune system becomes increasingly better at its task of recognizing and eliminating these pathogens (antigenic substances).
- The aiNet clusters will serve as internal images (mirrors) responsible for mapping existing clusters in the data set into network clusters. As an illustration, suppose there is a data set composed of three regions with high density of data, according to Figure 4(a). A hypothetical network architecture generated by the learning algorithm to be presented is shown in Figure 4(b). The numbers within the cells indicate their labels (the total number is generally higher than the number of clusters and much smaller than the number of samples), the numbers next to the connections represent their strengths, and dashed lines suggest connections to be pruned, in order to detect clusters and define the final network structure.
Figure 4:aiNet illustration. (a) Available data set with three clusters of high data density. (b) Network of labeled cells with their connection strengths assigned to the links. The dashed lines indicate connections to be pruned in order to generate disconnected sub-graphs, each characterizing a different cluster in the network.
- The aiNet clusters will serve as internal images (mirrors) responsible for mapping existing clusters in the data set into network clusters. As an illustration, suppose there is a data set composed of three regions with high density of data, according to Figure 4(a). A hypothetical network architecture generated by the learning algorithm to be presented is shown in Figure 4(b). The numbers within the cells indicate their labels (the total number is generally higher than the number of clusters and much smaller than the number of samples), the numbers next to the connections represent their strengths, and dashed lines suggest connections to be pruned, in order to detect clusters and define the final network structure.
