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A genetic algorithm-based dendritic cell algorithm for input signal generation

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Abstract

The dendritic cell algorithm (DCA) is a classification algorithm based on the biological antigen presentation process. Its classification efficiently depends on a data preprocessing procedure, where feature selection and signal categorization are the main work for generating input signals. Several methods have been employed (e.g., correlation coefficient and rough set theory). Those studies preferred to measure the importance of features by evaluating their relevance to the class. Generally, they determined a mapping relationship between important features and signal categories of DCA based on expert knowledge. Typically, those studies ignore the effect of unimportant features, and the mapping relationship determined by expertise may not produce an optimal classification result. Thus, a hybrid model, GA-DCA, is proposed for feature selection and signal categorization based on the genetic algorithm (GA). This study transforms feature selection and signal categorization into a grouping task (i.e., divides features into different signal groups). This study introduces a permutation-based expression with “Group" symbols to represent a potential feature grouping scheme. Correspondingly, adaptive operators are proposed to expand each possible scheme on the path from the initial feature grouping to the best feature grouping. GA-DCA searches the optimal feature subset and automatically assigns them to the most suitable signal groups without expertise. This study verifies the proposed approach by employing the UCI Machine Learning Repository and Keel-dataset Repository, and significant performance improvement is achieved.

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Data Availibility Statement

The datasets generated during and/or analysed during the current study are available in the UCI Machine Learning Repository, [http://archive.ics.uci.edu/ml] and Keel-dataset Repository, [http://www.keel.es/].

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Acknowledgements

The authors want to thank NSFC http://www.nsfc.gov.cn/ for the support through Grants Number 61877045, and Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants Number JCYJ2016042815-3956266.

Funding

This work was supported by NSFC http://www.nsfc.gov.cn/ (Grant numbers: 61877045) and Fundamental Research Project of Shenzhen Science and Technology Program (Grant numbers: JCYJ2016042815-3956266).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, No. 299 Bayi Road, Wuhan, Hubei Province, 430072, China

    Dan Zhang & Yiwen Liang

  2. GNSS Research Center, Wuhan University, No. 299 Bayi Road, Wuhan, Hubei Province, 430072, China

    Yu Zhang

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  1. Dan Zhang
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Correspondence to Dan Zhang.

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We declare that we have no conflict of interest in the submission of this manuscript, and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript.

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Zhang, D., Zhang, Y. & Liang, Y. A genetic algorithm-based dendritic cell algorithm for input signal generation. Appl Intell 53, 27571–27588 (2023). https://doi.org/10.1007/s10489-023-04819-7

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