Negative Selection Algorithm Research and Applications in the last decade: A Review
Not on the map. Why?
Abstract (arXiv)
The Negative selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems). Over the years, some progress was made which turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the last decade and categorizes those based on different characteristics and performances. Our study shows that NSA's evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains. We also present alternative approaches to NSA for comparison and analysis. It is evident that NSA performs better for nonlinear representation than most of the other methods, and it can outperform neural-based models in computation time. We summarize NSA's development and highlight challenges in NSA research in comparison with other similar models.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 174 references that are in the map. First posted on arXiv 13 May 2021.
Builds on
8 of 174 references.
References · 8
- 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
- 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
- 2020COPOD: Copula-Based Outlier Detectioncited by 146
- 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
- 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
- 2019XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learningcited by 42
- 2018LSCP: Locally Selective Combination in Parallel Outlier Ensemblescited by 32
- 2017Minimum Covariance Determinant and Extensionscited by 20
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Citing papers per year: 2022: 1.
1 paper.