Chi tiết| Tên | A Novel Combination of Negative and Positive Selection in Artificial Immune Systems |
| Lĩnh vực | Tin học |
| Tác giả | Nguyễn Văn Trường |
| Nhà xuất bản / Tạp chí | International Conference on Computing and Communication Technologies (RIVF) Năm 2013 |
| Số hiệu ISSN/ISBN | |
| Tóm tắt nội dung | |
Artificial Immune Systems (AIS) is a multidisciplinary research area that combines the principles of immunology and computation. Negative Selection Algorithms (NSA) is one of the most popular models of AIS mainly designed for one-class learning problems such as anomaly detection. Positive Selection Algorithms (PSA) is the twin brother of NSA with similar performance for AIS.
Both NSAs and PSAs comprise of two phases: generating a set D of detectors from a given set $S$ of selves (detector generation phase); and then detecting if a given cell (new data instance) is self or non-self using the generated detector set (detection phase). In this paper, we propose a novel approach to combining NSAs and PSAs that employ binary representation and r-chunk matching rule. The new algorithm achieves smaller detector storage complexity and potentially better detection time in comparison with single NSAs or PSAs.
Tải file A Novel Combination of Negative and Positive Selection in Artificial Immune Systems tại đây |
|
