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Negative selection

Negative selection is the process in which the immune system maturates T-cells, also known as T-lymphocytes, which make them capable of detecting non-self. Thus, the Negative Selection Algorithm (NSA) uses hyperspheres symbolizing the detectors in an N-dimensional data space. [1]

classes

  1. Binary version:

    The binary algorithm adapted for multiple classes in this project is based on the version proposed by Forrest et al. (1994), originally developed for computer security.

    Example:

  1. Real-Valued version:

    This algorithm has two different versions: one based on the canonical version [1] and another with variable radius detectors [3]. Both are adapted to work with multiple classes and have methods for predicting data present in the non-self region of all detectors and classes.

    Examples:

References

1​

BRABAZON, Anthony; O’NEILL, Michael; MCGARRAGHY, Seán. Natural Computing Algorithms. [S. l.]: Springer Berlin Heidelberg, 2015. DOI 10.1007/978-3-662-43631-8. Disponível em: http://dx.doi.org/10.1007/978-3-662-43631-8.

2​

S. Forrest, A. S. Perelson, L. Allen and R. Cherukuri, "Self-nonself discrimination in a computer," Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, USA, 1994, pp. 202-212, doi: http://dx.doi.org/10.1109/RISP.1994.296580.

3​

JI, Zhou; DASGUPTA, Dipankar. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. Genetic and Evolutionary Computation – GECCO 2004. [S. l.]: Springer Berlin Heidelberg, 2004. DOI 10.1007/978-3-540-24854-5_30. Disponível em: http://dx.doi.org/10.1007/978-3-540-24854-5_30.