文献类型：会议论文

英文题名：Pre-cut kNN Algorithm Based on Threshold of Distance

作者：Lu, Chen[1,2] Liang, Dong[1,2] Wang, Shan[1,2] Zeng, Lili[1,2] Zhao, Yilin[3]

第一作者：Lu, Chen

通信作者：Lu, C[1];Lu, C[2]

机构：[1]Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China;[2]Minist Educ, Key Lab Universal Wireless Commun, Beijing, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forestry, Beijing 100091, Peoples R China

会议论文集：6th IEEE International Conference on Network Infrastructure and Digital Content (IEEE IC-NIDC)

会议日期：AUG 22-24, 2018

会议地点：Guiyang, PEOPLES R CHINA

语种：英文

外文关键词：k-nearest neighbor; receiver operating curve; computational complexity; accuracy

年份：2018

摘要：In this paper, a novel approach to the k-Nearest Neighbors (kNN) algorithm is proposed. As one of the ten classical algorithms of data mining, KNN algorithm has a very good performance in classification problems such as pattern recognition. However, it undergoes an undeniable weakness that is high complexity, especially when the training set is huge. The motivation behind this proposed algorithm is to increase the computational efficiency of the traditional kNN algorithm, without sacrificing the accuracy, or even improve it. This key idea of the proposed algorithm is to pre-cut the comparison procedure of distance comparison through a predefined threshold. The experimental results reveal that this unproved pre-cut k NN algorithm, based on the threshold value of the smallest k distance, greatly increases computational efficiency, and do not cause any precision deduction, even improve an amount of accuracy. It can be concluded that this proposed algorithm achieves superior computational efficiency compared to the traditional kNN and previously proposed FkNN algorithm, especially when the data set is very large.