计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 152-158.doi: 10.11896/jsjkx.210300302
陈于思, 艾志华, 张清华
CHEN Yu-si, AI Zhi-hua, ZHANG Qing-hua
摘要： 邻域覆盖模型由于其原理简单以及对复杂数据具有较好的处理能力,在分类任务中得到了广泛应用。然而,邻域覆盖模型普遍存在运行效率较低的问题,且缺乏相关研究工作。为解决此问题,在传统邻域覆盖模型中引入距离间的三角不等式关系以提升构建邻域的效率,同时引入局部策略,定义了局部邻域覆盖以提升构建邻域覆盖的效率。为提升运行效率,从两个角度对传统邻域覆盖模型进行了改进,提出了基于三角不等式判定和局部策略的邻域覆盖模型(Neighborhood Covering Model based on Triangle Inequality Check and Local Strategy,TI-LNC)。此外,当前基于邻域覆盖模型的分类算法通常仅根据邻域中心以及邻域半径对样本进行分类,缺乏对邻域内样本信息的使用,从而影响了分类精度。为提高邻域覆盖模型的分类精度,增加了对邻域内样本信息的考虑,并基于TI-LNC设计了新的分类算法。在10个UCI数据集上的实验结果表明,所提模型能达到较高的运行效率以及较好的分类精度,具有一定的合理性及有效性。
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