Clustering and Retrieval Method of Immunological Memory Cell in Clonal Selection Algorithm
This work addresses the need for improved classification methods in medical databases, specifically for Coronary Heart Disease, but appears incremental as it builds on existing clonal selection algorithms.
The paper tackled the problem of implementing the clonal selection principle in a computational model by incorporating affinity maturation to create immunological memory cells for pathogen response, achieving a 99.6% classification accuracy on training data from a Coronary Heart Disease database.
The clonal selection principle explains the basic features of an adaptive immune response to a antigenic stimulus. It established the idea that only those cells that recognize the antigens are selected to proliferate and differentiate. This paper explains a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. Antibodies generated by the clonal selection algorithm are clustered in some categories according to the affinity maturation, so that immunological memory cells which respond to the specified pathogen are created. Experimental results to classify the medical database of Coronary Heart Disease databases are reported. For the dataset, our proposed method shows the 99.6\% classification capability of training data.