A Medical Text Classification System Based on Immune Algorithm

Abstract

This paper proposes a new method of text categorization called the clonal selection algorithm based on antibody density (CSABAD). In this method, antigen, B cell and antibody are respectively corresponded with training texts, a possible individual of classifier and the affinity between the individual and training texts. B cells consist of general cells, fresh cells and memory cells. General cells are used to store various individuals, fresh cells are used to replace the degraded general cells, and memory cells are used to record the best individuals. According to the clonal selection principle and density control mechanism, only those cells that have higher affinity and lower density are selected to proliferate. The ultimate classifier is composed with many memory cells. Considering the characters of medical information, we realize a medical text classifier based on CSABAD, and tests the system on OHSUMED data set. The experiment results show that it can obtain the better classification performance.