2014 IEEE Global High Tech Congress on Electronics (GHTCE 2014)

95 / 2014-09-14 22:02:32

An Improved Model Based On Negative Selection Algorithm

negative selection algorithm; artificial immune; semi-supervised learning

The widely used negative selection algorithm is one of the important algorithms of artificial immune system. However, there are also some disadvantages, such as insufficient learning of self-tolerance in the circumstance of small training set, which affects the detection accuracy. We use a semi-supervised learning mechanism to solve the inadequate learning problem, expand the training sample source, make training to learn more representative samples. Simulation experiments prove that the semi-supervised learning algorithm can improve the training learning process, improve the detection rate, and have strong adaptive capacity.