The condensed nearest neighbor (CNN) is a pioneering instance selection algorithm for 1-nearest neighbor. Many variants of CNN for K-nearest neighbor have been proposed by different researchers. However, few studies were conducted on condensed fuzzy K-nearest neighbor. In this paper, we present a condensed fuzzy K-nearest neighbor (CFKNN) algorithm that starts from an initial instance set S and iteratively selects informative instances from training set T, moving them from T to S. Specifically, CFKNN consists of three steps. First, for each instance x ∈ T, it finds the K-nearest neighbors in S and calculates the fuzzy membership degrees of the K nearest neighbors using S rather than T. Second it computes the fuzzy membership degrees of x using the fuzzy K-nearest neighbor algorithm. Finally, it calculates the information entropy of x and selects an instance according to the calculated value. Extensive experiments on 11 datasets are conducted to compare CFKNN with four state-of-the-art algorithms (CNN, edited nearest neighbor (ENN), Tomeklinks, and OneSidedSelection) regarding the number of selected instances, the testing accuracy, and the compression ratio. The experimental results show that CFKNN provides excellent performance and outperforms the other four algorithms.

中文翻译：

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模糊K最近邻的实例选择算法

压缩最近邻居（CNN）是一种用于1-最近邻居的开创性实例选择算法。不同研究人员已经提出了针对K近邻的CNN的许多变体。然而，很少有关于凝聚态模糊K近邻的研究。在本文中，我们提出了一种压缩模糊K最近邻算法（CFKNN），该算法从初始实例集S开始，然后从训练集T中迭代地选择信息量实例，将它们从T移到S。具体地说，CFKNN包括三个步骤。首先，对于每个实例x∈T，它找到S中的K个最近邻，并使用S而不是T计算K个最近邻的模糊隶属度。其次，它使用模糊K近邻计算x的模糊隶属度。邻居算法。最后，它计算x的信息熵，并根据计算出的值选择一个实例。在11个数据集上进行了广泛的实验，以将CFKNN与四种最新算法（CNN，已编辑最近邻（ENN），Tomeklinks和OneSidedSelection）进行比较，以了解所选实例的数量，测试准确性和压缩率。实验结果表明，CFKNN具有出色的性能，并且优于其他四种算法。