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Artificial Immune Systems

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Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 118))

Abstract

We introduce Artificial Immune Systems by emphasizing on their ability to provide an alternative machine learning paradigm. The relevant bibliographical survey is utilized to extract the formal definition of Artificial Immune Systems and identify their primary application domains, which include:

  • Clustering and Classification,

  • Anomaly Detection/Intrusion Detection,

  • Optimization,

  • Automatic Control,

  • Bioinformatics,

  • Information Retrieval and Data Mining,

  • User Modeling/Personalized Recommendation and

  • Image Processing.

Special attention is paid on analyzing the Shape-Space Model which provides the necessary mathematical formalism for the transition from the field of Biology to the field of Information Technology. This chapter focuses on the development of alternative machine learning algorithms based on Immune Network Theory, the Clonal Selection Principle and the Theory of Negative Selection. The proposed machine learning algorithms relate specifically to the problems of:

  • Data Clustering,

  • Pattern Classification and

  • One-Class Classification.

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  1. University of Piraeus, Piraeus, Greece

    Dionisios N. Sotiropoulos & George A. Tsihrintzis

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  1. Dionisios N. Sotiropoulos
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  2. George A. Tsihrintzis
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Correspondence to Dionisios N. Sotiropoulos .

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Sotiropoulos, D.N., Tsihrintzis, G.A. (2017). Artificial Immune Systems. In: Machine Learning Paradigms. Intelligent Systems Reference Library, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-319-47194-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-47194-5_7

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