有序规范实数对多相似度K最近邻分类算法  

Multi-similarity K-nearest neighbor classification algorithm with ordered pairs of normalized real numbers

作　　者：崔昊阳 张晖[2] 周雷[2] 杨春明[1] 李波[1] 赵旭剑[1] CUI Haoyang;ZHANG Hui;ZHOU Lei;YANG Chunming;LI Bo;ZHAO Xujian(School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang Sichuan 621010,China;School of Mathematics and Physics,Southwest University of Science and Technology,Mianyang Sichuan 621010,China)

机构地区：[1]西南科技大学计算机科学与技术学院,四川绵阳621010 [2]西南科技大学数理学院,四川绵阳621010

出　　处：《计算机应用》2023年第9期2673-2678,共6页journal of Computer Applications

基　　金：四川省科技厅重点研发项目(2021YFG0031);四川省省级科研院所科技成果转化项目(2022JDZH0035)。

摘　　要：针对最近邻分类算法性能受到所采用的相似度或距离度量方法影响大,且难以选择最优的相似度或距离度量方法的问题,提出一种采用多相似度的基于有序规范实数对的K最近邻分类算法(OPNs-KNN)。首先,在机器学习领域中引入有序规范实数对(OPN)这一新的数学理论,利用多种相似度或距离度量方法将训练集和测试集中所有样本全部转换为OPN,使每个OPN均包含不同的相似度信息;然后再通过改进的最近邻算法对OPN进行分类,实现不同相似度或距离度量方法的结合与互补,从而提高分类性能。实验结果表明,在Iris、seeds等数据集上与距离加权K近邻规则(WKNN)等6种最近邻分类的改进算法相比,OPNs-KNN的分类准确率提高了0.29~15.28个百分点,验证了所提算法能大幅提升分类的性能。For the problems that the performance of the nearest neighbor classification algorithm is greatly affected by the adopted similarity or distance measuring method,and it is difficult to select the optimal similarity or distance measuring method,with multi-similarity method adopted,a K-Nearest Neighbor algorithm with Ordered Pairs of Normalized real numbers(OPNs-KNN)was proposed.Firstly,the new mathematical theory of Ordered Pair of Normalized real numbers(OPN)was introduced in machine learning.And all the samples in the training and test sets were converted into OPNs by multiple similarity or distance measuring methods,so that different similarity information was included in each OPN.Then,the improved nearest neighbor algorithm was used to classify the OPNs,so that different similarity or distance measuring methods were able to be mixed and complemented to improve the classification performance.Experimental results show that compared with 6 improved nearest neighbor classification algorithms,such as distance-Weighted K-Nearest-Neighbor rule(WKNN)rule on Iris,seeds,and other datasets,OPNs-KNN has the classification accuracy improved by 0.29 to 15.28 percentage points,which proves that the performance of classification can be improved greatly by the proposed algorithm.

关 键 词：机器学习 最近邻算法 多相似度 分类算法 有序规范实数对 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]