期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：4
语种：English
出版社：IAENG - International Association of Engineers
摘要：K-nearest neighbor (KNN) rule is a very simple and efficient non-parametric classification algorithm that is widely used in machine learning. In this paper, we proposed a attribute weighting local-mean pseudo nearest neighbor rule (AWLMPNN). The main difference of AWLMPNN and local mean-based pseudo nearest neighbor (LMPNN) is that they use attribute weighting distance and Euclidean distance to measure the distance between two samples, respectively. To illustrate the effectiveness of the proposed AWLMPNN method, extensive experiments on 30 real UCI data sets are conduced by comparing with four competing KNN-based methods. The experimental results show that the proposed AWLMPNN method is superior to other methods, especially in the case of high dimensional attributes with small sample size.
关键词：K nearest neighbors;local mean vector;attribute weighting
