Comparing binary and real-valued coding in hybrid immune algorithm for feature selection and classification of ECG signals

The paper presents a new algorithm for feature selection and classification. The algorithm is based on an immune metaphor, and combines both negative and clonal selection mechanisms characteristic for B- and T-lymphocytes. The main goal of the algorithm is to select the best subset of features for classification. Two level evolution is used in the proposed system for detectors creation and feature selection. Subpopulations of evolving detectors (T-lymphocytes) are able to discover subsets of features well suited for classification. The subpopulations cooperate during evolution by means of a novel suppression mechanism which is compared to the traditional suppression mechanism. The proposed suppression method proved to be superior to the traditional suppression in both recognition performance and its ability to select the proper number of subpopulations dynamically. Some results in the task of ECG signals classification are presented. The results for binary and real coded T-lymphocytes are compared and discussed.

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