广东工业大学学报2017，Vol.34Issue(3)：1-7,7.DOI:10.12052/gdutxb.170008
基于正交投影的降维分类方法研究
Classification Method Based on Dimension Reduction
摘要
Abstract
Data mining algorithm in the era of big data needs to be able to efficiently deal with massive data. Traditional classification algorithms take a long time to train a model and classify the test dataset, and the algorithm is difficult to understand. To deal with the problems, a classification method based on dimension reduction is proposed in this paper. The multidimensional classification problem is transformed into multiple 2D projection surface combination by projection, and a density model of the projection surface is trained for classification. Compared with Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbor (KNN) and other algorithms, the classification method based on dimension reduction has higher training efficiency and classification efficiency without loss of accuracy. The method is easy to implement, so it can be used for real-time application, such as intrusion detection and traffic scheduling.关键词
数据挖掘/分类/正交投影/降维Key words
data mining/classification/orthogonal projection/dimension reduction分类
信息技术与安全科学引用本文复制引用
滕少华,卢东略,霍颖翔,张巍..基于正交投影的降维分类方法研究[J].广东工业大学学报,2017,34(3):1-7,7.基金项目
国家自然科学基金资助项目(61402118,61673123) （61402118,61673123）
广东省科技计划项目(2015B090901016,2016B010108007) （2015B090901016,2016B010108007）
广东省教育厅项目(粤教高函2015[133]号,粤教高函〔2014〕97号) （粤教高函2015[133]号,粤教高函〔2014〕97号）
广州市科技计划项目(201604020145,2016201604030034, 201508010067) （201604020145,2016201604030034, 201508010067）
