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

---

Negative Selection can be applied in different contexts, such as:

• Anomaly detection
• Classification

Package implementation

Binary Negative Selection Algorithm (BNSA)

The binary algorithm adapted for multiple classes in this project is based on the version proposed by Forrest et al. (1994)², originally developed for computer security.

Real-Valued Negative Selection Algorithm (RNSA)

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.

References

Footnotes

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. Available at: https://dx.doi.org/10.1007/978-3-662-43631-8. ↩ ↩²

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: https://dx.doi.org/10.1109/RISP.1994.296580. ↩

3. Ji, Z.; Dasgupta, D. (2004). Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. In Lecture Notes in Computer Science, vol. 3025. https://doi.org/10.1007/978-3-540-24854-5_30 ↩