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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

期数:

2012年05期

页码:

83-86

栏目:

智能、算法、系统工程

出版日期:

1900-01-01

文章信息/Info

Title:

A New Text Classification Algorithm One Class SVM-KNN

文章编号:

1673-629X（2012）05-0083-04

作者:

刘文 吴陈

江苏科技大学智能信息处理实验室

Author(s):

LIU Wen;  WU Chen

The Opening Laboratory of Intelligent Computing, Jiangsu University of Science and Technology

关键词:

中文文本分类; 支持向量机; K-近邻; One; Class; SVM—KNN

Keywords:

Chinese text classification;  support vector machine;  K-nearest neighbour;  One Class SVlVI-KNN

分类号:

TP301.6

文献标志码:

A

摘要:

中文文本分类在数据库及搜索引擎中得到广泛的应用，K-近邻（KNN）算法是常用于中文文本分类中的分类方法，但K-近邻在分类过程中需要存储所有的训练样本，并且直到待测样本需要分类时才建立分类，而且还存在类倾斜现象以及存储和计算的开销大等缺陷。单类SVM对只有一类的分类问题具有很好的效果，但不适用于多类分类问题，因此针对KNN存在的缺陷及单类SVM的特点提出OneClassSVM—KNN算法，并给出了算法的定义及详细分析。通过实验证明此方法很好地克服了KNN算法的缺陷，并且查全率、查准率明显优于K-近邻算法

Abstract:

Text classification is widely used in database and search engine. KNN is widely used in Chinese text categorization,however, KNN has many defects in the application of text classification. The deficiency of KNN classification algorithm is that all the training sam- pies are kept until the samples are classified. When the size of samples is very large, the storage and computation will be costly, which will result in classification deviation. One class SVM is a simple and effective classification algorithm in one class. To solve KNN problems, a new algorithm based on harmonic one-class-SVM and KNN was proposed,which will achieve better classification effect. The experiment result is shown that the recall computed using the proposed method is obviously more highly than the KNN method

备注/Memo

备注/Memo:

刘文（1985-），男，山东临沂人，硕士研究生，研究方向为数据挖掘；吴陈，教授，研究方向为数据挖掘、模式识别等

常用功能

更新日期/Last Update: 1900-01-01