The $k$-nearest neighbor (kNN) rule is a classical non-parametric classification algorithm in pattern recognition, and has been widely used in many fields due to its simplicity, effectiveness and intuitiveness. However, the classification performance of the kNN algorithm suffers from the choice of a fixed and single value of $k$ for all queries in the search stage and the use of simple majority voting rule in the decision stage.

In this paper, we propose a new kNN-based algorithm, called locally adaptive $k$-nearest neighbor algorithm based on discrimination class (DC-LAKNN). In our method, the role of the second majority class in classification is for the first time considered. Firstly, the discrimination classes at different values of $k$ are selected from the majority class and the second majority class in the $k$-neighborhood of the query. Then, the adaptive $k$ value and the final classification result are obtained according to the quantity and distribution information on the neighbors in the discrimination classes at each value of $k$.

Extensive experiments on eighteen real-world datasets from UCI (University of California, Irvine) Machine Learning Repository and KEEL (Knowledge Extraction based on Evolutionary Learning) Repository show that the DC-LAKNN algorithm achieves better classification performance compared to standard kNN algorithm and nine other state-of-the-art kNN-based algorithms.

的 $ķ$近邻（kNN）规则是模式识别中的一种经典的非参数分类算法，由于其简单，有效和直观性，已在许多领域得到了广泛的应用。但是，kNN算法的分类性能受到选择固定和单一值的影响。$ķ$ 搜索阶段的所有查询，以及决策阶段使用简单多数投票的规则。

在本文中，我们提出了一种新的基于kNN的算法，称为局部自适应 $ķ$区分等级的近邻算法（DC-LAKNN）。在我们的方法中，首次考虑了第二多数类在分类中的作用。首先，不同等级的歧视等级$ķ$ 选自多数派和第二多数派 $ķ$-查询的邻域。然后，自适应$ķ$ 值和最终分类结果是根据每个分类的值在判别类中的邻居的数量和分布信息获得的 $ķ$。

对来自UCI（加利福尼亚大学欧文分校）机器学习存储库和KEEL（基于进化学习的知识提取）存储库的18个真实世界数据集的广泛实验表明，与标准kNN算法和其他9个相比，DC-LAKNN算法具有更好的分类性能。基于kNN的最新算法。