Abstract: Artificial immune systems utilize training samples to screen and train candidate detectors,so as to generate mature detectors covering non-self regions for self and non-self differentiation.The traditional negative selection algorithm(NSA) based detector 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 address this problem,this paper proposes an immune detector training method based on label influence propagation,where label influence 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 labeled samples based on noise-learning-based pseudo-labeling assessment.The newly labeled samples that passed the labeling assessment are added to the training sample set to extend the labeled sample size and improve the training quality of the immune detector.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 performance 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,GFNSA,etc,the recognition accuracy of the detector is improved by 10% on average.

Key words: Label influence propagation, Artificial immunity, Detector generation algorithms, Label evaluation