An Improved Support Vector Machine Technology for Meta-Information Classification

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An Improved Support Vector Machine Technology for Meta-Information Classification

更新时间：2025-07-09

- An Improved Support Vector Machine Technology for Meta-Information Classification
- Vol. 45, Issue 8, Pages: 37-42(2011)
- 作者机构：
  
  西北工业大学计算机学院,西安,710072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393.0
- Online First：10 August 2011，
  
  Published：2011
- 稿件说明：
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An Improved Support Vector Machine Technology for Meta-Information Classification[J]. 2011, 45(8): 37-42.
DOI：

An Improved Support Vector Machine Technology for Meta-Information Classification[J]. 2011, 45(8): 37-42. DOI：

摘要

针对传统元信息分类方法的准确率不能满足主动P2P网络监测模型要求的问题

提出了一种基于改进支持向量机算法的元信息分类方法.该方法首先通过在加权最小二乘支持向量机的基础上加入对数据偏斜的处理

解决了元信息分类时关键词特征稀疏和样本高度不均衡问题

在对元信息文件名进行分词时

加入了词条之间的组合关系处理

在进行特征向量表示时

加入了对词条权值和语义属性的处理

最后使用基于粗糙集的属性规约方法进行特征向量选择

有效地降低了特征向量维度.实验结果表明

与传统方法相比

所提方法在进行元信息分类时能够大幅度提高分类准确率

准确率可达到97.8%

完全能够满足主动P2P网络监测模型的要求.

Abstract

An improved support vector machine(SVM)algorithm for meta-information classification is proposed to solve the problem that the traditional classification algorithm on meta-information can't meet the requirement of initiative P2P network monitoring model. The algorithm solves the problems that the keywords in classification are sparse and the distribution of sample is unbalanced by adding processing the data skew on LS-VSM. Combination relationships among keywords are added on filename segmentation of the meta-information. The weights of keywords and semantic attribute are processed for feature vector representation and the feature vector is selected using rough set specification so that the dimension of the feature vector is effectively reduced. Experimental results and comparisons with the traditional algorithm show that the proposed algorithm achieves a classification accuracy at 97.8%

that is the algorithm dramatically improves the classification accuracy

and meets the requirement of initiative P2P network monitoring model satisfactorily.

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references

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