自适应半径选择的近邻邻域分类器

Near Neighborhood Classifier with Adaptive Radius Selection

在邻域粗糙集中,邻域分类器简单高效.然而,邻域半径作为决定邻域分类器分类性能的关键因素,构建方式存在不足.一方面,邻域半径的构建由于未经过训练过程而缺乏通用性;另一方面,当数据样本分布不均出现空邻域时可导致分类器失效.针对上述问题,文中提出自适应半径选择的近邻邻域分类器(Near Neighborhood Classifier with Adaptive Radius Selection,NNC-AR).首先,基于K近邻算法为训练样本构建训练邻域半径.然后,为了克服传统方法选取邻域半径参数的主观性,对待测试样本定义自适应的近邻邻域半径.最后,为分类器失效的部分测试样本定义新的近似邻域半径,有效提升分类器的泛化能力.实验表明,NNC-AR的F1值和分类精度均较高.

In neighborhood rough sets,the neighborhood classifier is an intuitive and effective classification method in data mining.However,the neighborhood radius,as a key factor to determine the classification performance of the neighborhood classifier,has some defects in construction.The construction of the neighborhood radius is not universal due to the lack of the training stage.When the data samples are not uniformly distributed and then empty neighborhoods appear,the classifier fails.Aiming at these problems,a near neighborhood classifier with adaptive radius selection(NNC-AR)is proposed.Firstly,for training samples,a training neighborhood radius based on K-nearest neighbor is defined.For test samples,an adaptive neighborhood radius is defined to overcome the subjectivity of the artificial parameter in the traditional neighborhood radius.Finally,for the test samples with classifier failure,an approximate neighborhood radius is defined to improve the generalization ability of the classifier.The experimental results show that the F1 score and classification accuracy of NNC-AR model are significantly improved.