A Medical Text Classification System Based on Immune Algorithm

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 individua...

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Bibliographic Details
Main Authors: Qirui Zhang, Man Luo, Hexian Wang, Jinghua Tan
Format: Conference Proceeding
Language:English
Subjects:
affinity
Biomedical engineering
clonal selection
Control systems
Educational institutions
Frequency
immune algorithm
Immune system
Machine learning
Machine learning algorithms
medical information
memory cells
Pattern recognition
Seminars
Text categorization
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Summary: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.
ISSN:2157-9598
2157-9601
DOI:10.1109/FBIE.2008.82