## 3 Our Proposal: An Artificial Immune System for Associative Classification
### AISAC: Artificial Immune System for Associative Classification  
The main goal of classifier systems is to assign or predict a class label for an unseen pattern according to its attributes after training it with similar patterns [40]. Many classifier systems are based on two steps: model construction and model operation [41]. Model construction is a representation of the training set, which is used to generate a structure to classify the patterns presented. Model operation classifies data whose class labels are not known using the previously constructed model. In this paper, the classification problem is addressed by proposing a new model based on the immune system. Although numerous computational models have been developed based on the human immune system [42], this research presents a new model that incorporates additional elements of the immune response. The model is called Artificial Immune System for Associative Classification (AISAC). This model is proposed within the supervised classification paradigm, and therefore constitutes a new supervised classifier. Among the characteristics of the proposed model, it can be highlighted that it is an eager classifier, since it generates internal data structures to classify the new instances. Thus, the training set is replaced by other structures. The proposed artificial immune system model includes two types of functions: the acquired (adaptive) immune response, and the innate immune response. The acquired immune response consists of five phases:  
1. Detection of antigenic macromolecules;
2. Activation of B lymphocytes;
3. Immune response regulation;
4. Development of adaptive immunity;
5. Resolution of the threat. The innate immune response has only one phase:Resolution of the threat. In general, the acquired immunological response begins with the detection of antigenic macromolecules by macrophages. Each macrophage will phagocytose a number of antigenic determinants of the antigenic molecule in which it specializes. Subsequently, each macrophage will present the antigenic determinants that it phagocytosed to the T lymphocyte helpers. In Phase 2, these lymphocytes will generate an immune response, activating a certain number of B lymphocytes. Activated B lymphocytes will produce and release specific antibodies to the antigens presented by the macrophage. Then, the immune response will be monitored (Phase 3). If the immune response is satisfactory, the generated antibodies are conserved. Otherwise, a readjustment of the generated antibodies is performed so that they are able to combine with the antigenic determinants presented. To guarantee the development of adaptive or acquired immunity (Phase 4), each of the antibodies will undergo a reconstitution phase so that it is capable of improving its immune response. Finally, in Phase 5, the antigenic macromolecules are completely removed and the antigens are stored in the immune memory. In the case of the innate immune response a set of antibodies is already in memory; when an antigen is present, it is automatically detected and eliminated. Metaphor-Free AISAC  
The proposed artificial immune system-based model is indeed a classification algorithm. The acquired immune response corresponds to the model construction phase of the classifier, and the innate immune response corresponds to the model operation phase of the algorithm. Thus, the training phase consists of five steps:  
1. Detection of antigenic macromolecules. For each class (antigenic macromolecule), a selected number of instance bags (macrophages) will be computed. Then, the instances of the class will be randomly assigned to the bags;
2. Activation of B lymphocytes. For each bag of each class, a prototype (B lymphocyte) will be computed, considering the mean of the instances in the corresponding bag;
3. Immune response regulation. The prototypes computed in phase 2 are moved in the space by using the training set classification performance as an adaptability function;
4. Development of adaptive immunity.