MILA — Multilevel Immune Learning Algorithm View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2003-06-18

AUTHORS

Dipankar Dasgupta , Senhua Yu , Nivedita Sumi Majumdar

ABSTRACT

The biological immune system is an intricate network of specialized tissues, organs, cells, and chemical molecules. T-cell-dependent humoral immune response is one of the complex immunological events, involving interaction of B cells with antigens (Ag) and their proliferation, differentiation and subsequent secretion of antibodies (Ab). Inspired by these immunological principles, we proposed a Multilevel Immune Learning Algorithm (MILA) for novel pattern recognition. It incorporates multiple detection schema, clonal expansion and dynamic detector generation mechanisms in a single framework. Different test problems are studied and experimented with MILA for performance evaluation. Preliminary results show that MILA is flexible and efficient in detecting anomalies and novelties in data patterns. More... »

PAGES

183-194

References to SciGraph publications

  • 1999. An Anomaly Entection Algorithm Inspired by the Immune Syste in ARTIFICIAL IMMUNE SYSTEMS AND THEIR APPLICATIONS
  • Book

    TITLE

    Genetic and Evolutionary Computation — GECCO 2003

    ISBN

    978-3-540-40602-0
    978-3-540-45105-1

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-45105-6_24

    DOI

    http://dx.doi.org/10.1007/3-540-45105-6_24

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016881069


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