Adaptive Weight Optimization of Dendritic Cell Algorithm
Dendritic Cell Algorithm is a classic algorithm in the innate immune layer of artificial immune systems,which detect anomalies by integrating danger and safe signals.However,the dendritic cell algorithm often requires manual setting of signal weights based on data features,thereby diminishing its adaptability.To address this issue,a grid search method which can automatically adjust weights within a given range based on detection accuracy has been introduced in order to obtain adaptive signal weights suitable for different types and scales of datasets.Experimental results on several public datasets demonstrated that the adaptive weight optimized dendritic cell algorithm can train reasonable weight matrices according to characteristics of dataset,thus reducing the impact of human experience on algorithm accuracy.The improved algorithm exhibits higher detection accuracy and true positive rates than the original dendritic cell algorithm and perform better than similar algorithms.
adaptive weightgrid searchdendritic cell algorithm(DCA)artificial immune system
万方数据
维普
