Among the extensions of twin support vector machine (TSVM), some scholars
have utilized K-nearest neighbor (KNN) graph to enhance TSVM's classification
accuracy. However, these KNN-based TSVM classifiers have two major issues such
as high computational cost and overfitting. In order to address these issues,
this paper presents an enhanced regularized K-nearest neighbor based twin
support vector machine (RKNN-TSVM). It has three additional advantages: (1)
Weight is given to each sample by considering the distance from its nearest
neighbors. This further reduces the effect of noise and outliers on the output
model. (2) An extra stabilizer term was added to each objective function. As a
result, the learning rules of the proposed method are stable. (3) To reduce the
computational cost of finding KNNs for all the samples, location difference of
multiple distances based k-nearest neighbors algorithm (LDMDBA) was embedded
into the learning process of the proposed method. The extensive experimental
results on several synthetic and benchmark datasets show the effectiveness of
our proposed RKNN-TSVM in both classification accuracy and computational time.
Moreover, the largest speedup in the proposed method reaches to 14 times.

该研究提出了一种增强的、基于加权的正则化 KNN 双支持向量机，通过加权样本、增加稳定项和结合多距离 KNN 算法来解决 KNN-based TSVM 分类器的高计算成本和过拟合等问题，实验结果表明在分类准确度和计算时间上均有显著提高，最大加速比可达 14 倍。