Study on Artificial Immune Detector Generation Algorithm Based on Label Influence Propagation
Artificial immune systems utilize training samples to screen and train candidate detectors,so as to generate mature de-tectors covering non-self regions for self and non-self differentiation.The traditional negative selection algorithm(NSA)based de-tector generation algorithm usually requires a large number of labeled self training samples,while the limited number of labeled samples in practical applications leads to insufficient detector training,which restricts the detection accuracy of detectors.To ad-dress this problem,this paper proposes an immune detector training method based on label influence propagation,where label in-fluence propagation is performed by a small number of labeled cluster members among samples belonging to the same cluster,and pseudo-labeling is performed for the unlabeled samples in the cluster.Subsequently,this paper removes low-confidence newly la-beled samples based on noise-learning-based pseudo-labeling assessment.The newly labeled samples that passed the labeling as-sessment are added to the training sample set to extend the labeled sample size and improve the training quality of the immune de-tector.Comparative experimental results on seven types of UCI public datasets of different dimensions and sizes show that the proposed label influence propagation-based immune detection training algorithm is able to effectively improve the training per-formance of the detector,especially in the case of limited training samples or unbalanced datasets,the detector's performance is significantly better than the traditional methods.Compared with the detection generation algorithms such as PSA,co-PSA,GFN-SA,etc,the recognition accuracy of the detector is improved by 10％on average.
万方数据
维普
