A negative selection algorithm generates detectors to realize abnormality detection by simulating the maturation process of T cells in human immunity. Holes are areas of feature space that cannot be covered by the detector set and are the major factor in the degradation of algorithm performance. Conventional methods alleviate the hole problem by minimizing the coverage area of the holes. In this study, we approach the issue from a different angle. Holes are prone to form in the boundary area between the self and nonself regions, and when the self and the nonself cross or overlap, the hole problem becomes more serious. The k-nearest neighbors (k-NN) algorithm is more suitable than other methods for pending instance sets where the class domain crosses or overlaps more. Therefore, we propose a hybrid real-valued negative selection algorithm with variable-sized detectors (V-Detector) and the k-NN algorithm, abbreviated as V-Detector-kNN. The V-Detector-kNN hybrid algorithm first uses the V-Detector algorithm to classify, and then, for the problem that the nonself instances in the holes are misclassified as selfs, k-NN is introduced to classify those misclassified instances to improve the detection rate. Theoretical analysis proves that the V-Detector-kNN algorithm that we proposed has a higher detection rate than the V-Detector algorithm in most cases. Comparative experiments with 5 different algorithms on 9 UCI datasets show that our proposed algorithm ranks first in detection rate.

负选择算法生成检测器，通过模拟人体免疫中T细胞的成熟过程来实现异常检测。空洞是检测器集无法覆盖的特征空间区域，是算法性能下降的主要因素。传统方法通过最小化孔洞的覆盖区域来缓解孔洞问题。在这项研究中，我们从不同的角度处理这个问题。在自体和非自体区域之间的边界区域容易形成孔洞，当自体和非自体交叉或重叠时，孔洞问题变得更加严重。k-最近邻（k-NN）算法比其他方法更适合类域交叉或重叠更多的待处理实例集。所以，我们提出了一种具有可变大小检测器（V-Detector）和k-NN算法的混合实值负选择算法，缩写为V-Detector-kNN。V-Detector-kNN混合算法首先使用V-Detector算法进行分类，然后针对孔洞中的nonself实例被误分类为self的问题，引入k-NN对那些误分类的实例进行分类以提高检测效果速度。理论分析证明，我们提出的V-Detector-kNN算法在大多数情况下比V-Detector算法有更高的检测率。在 9 个 UCI 数据集上使用 5 种不同算法的对比实验表明，我们提出的算法在检测率方面排名第一。V-Detector-kNN混合算法首先使用V-Detector算法进行分类，然后针对孔洞中的nonself实例被误分类为self的问题，引入k-NN对那些误分类的实例进行分类以提高检测效果速度。理论分析证明，我们提出的V-Detector-kNN算法在大多数情况下比V-Detector算法有更高的检测率。在 9 个 UCI 数据集上使用 5 种不同算法的对比实验表明，我们提出的算法在检测率方面排名第一。V-Detector-kNN混合算法首先使用V-Detector算法进行分类，然后针对孔洞中的nonself实例被误分类为self的问题，引入k-NN对那些误分类的实例进行分类以提高检测效果速度。理论分析证明，我们提出的V-Detector-kNN算法在大多数情况下比V-Detector算法有更高的检测率。在 9 个 UCI 数据集上使用 5 种不同算法的对比实验表明，我们提出的算法在检测率方面排名第一。理论分析证明，我们提出的V-Detector-kNN算法在大多数情况下比V-Detector算法有更高的检测率。在 9 个 UCI 数据集上使用 5 种不同算法的对比实验表明，我们提出的算法在检测率方面排名第一。理论分析证明，我们提出的V-Detector-kNN算法在大多数情况下比V-Detector算法有更高的检测率。在 9 个 UCI 数据集上使用 5 种不同算法的对比实验表明，我们提出的算法在检测率方面排名第一。