基于正交投影的降维分类方法研究

Classification Method Based on Dimension Reduction

大数据时代要求数据挖掘算法能高效处理海量数据,针对传统分类算法建模时间长、分类耗时久、算法难以理解等问题,提出一种基于正交投影的降维分类方法,通过投影方式将多维分类问题转化为多个二维投影面组合问题,建立投影面的密度模型进行分类.相比于常用的支持向量机(Support Vector Machine,SVM)、逻辑回归(Logistic Regression,LR)、k最近邻(K-Nearest Neighbor,KNN)等分类算法,基于正交投影降维的分类方法能够在不丢失分类准确度的同时,拥有较高的模型训练效率与分类效率.其算法易于实现,可用于实时性要求较高的应用场合,如入侵检测,交通调度等.

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 multiple2D 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.