中文摘要：

当训练样本分布密集交错时,传统的否定选择算法难以将检测器生成在正/反样本间的有效区域,导致检测器集合对这些样本的识别率降低,影响了算法性能.为使检测器能有效地识别分布密集交错的样本,提出了免疫进化否定选择算法（IENSA）.IENSA通过加入两个免疫进化过程,首先在样本分布密集的区域引导检测器在正/反样本之间有效地生成,然后在样本分布稀疏的区域对冗余检测器进行抑制.实验结果表明在二维人工数据集Rectangle与三维标准数据集Skinsegmentation上,相对于经典的RNSA与V-detector算法,IENSA均能以较少的检测器达到较高的检测率.

英文摘要：

When the samples distr ibute densely, the traditional negative selection algorithm is difficult to generthe gap bet-ween normal and abnormal samples, it causes that the algorithm has the low detecting rate for these samples. In or-der to enable the detector to effectively identify the densely samples, this paper proposed the immune evolution negative selec-tion algorithm （IE^NSA） . By adding two immune evolution processes, IENSA could generate detector in the gap bet-wemal and abnormal samples ef fect ively , and restrain the redundant detector in the sparse area of the sample distr ib ution . Theexperimental result showthat, on the artificial data set Rectangle （2D ） and the UCI standard data set Skin segmentation（ 3D ）,compared to the classical RNSA and V-detector algorithm, IENSA can reach the higher detection rate with the less an-tibodies and training time.