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Classification of Objects Based on a Tree-Shaped Artificial Immune Network Model

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Advances in Intelligent Systems and Computing V (CSIT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1293))

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Abstract

This paper describes the solution of the object classification problem in a multidimensional attribute space based on a modified artificial immune network model using the principles of a minimum spanning tree (MST). During the classification with unsupervised learning (clustering) at different execution stages of the immune network, objects are used as antigens and antibodies to form a training sample (classifier). In the case of classification with supervised learning, objects from a training set are used as a set of antigens, and objects to classification are used as a set of antibodies. The class definition for each object is based on the avidity value, which describes the strength of cooperative affinity interactions of antibodies with antigen. Using the proposed model allows to speed up the classification process in comparison with models based on the MST and C-means methods, as well as automating the process of determining the number of classes in the absence of a training sample.

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Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Mykola Korablyov, Oleksandr Fomichov & Natalia Axak

Authors
  1. Mykola Korablyov
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  2. Oleksandr Fomichov
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  3. Natalia Axak
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Corresponding author

Correspondence to Mykola Korablyov .

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Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Korablyov, M., Fomichov, O., Axak, N. (2021). Classification of Objects Based on a Tree-Shaped Artificial Immune Network Model. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_11

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