采用类别相似度聚合的关联文本分类方法

田丰 1; 桂小林 1; 杨攀 1; 王刚 1; 2; 3; 郭岳龙 1; 2

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采用类别相似度聚合的关联文本分类方法

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- 采用类别相似度聚合的关联文本分类方法
- Associative Rule-Based Text Categorization Method Using Category Similarity
- 西安交通大学学报 2012年46卷第12期页码：6-11+122
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 西安交通大学陕西省计算机网络重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2012-03-31. 作者简介: 田丰(1987-),男,博士生;桂小林(通信作者),男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(60873071,61172090);国家高技术研究发展计划重大专项资助项目(2012ZX03002001-004)
- DOI：
  中图分类号： TP391
- 网络首发：2012-12-10，
  
  纸质出版：2012
- 稿件说明：
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田丰 1, 桂小林 1, 杨攀 1, 等. 采用类别相似度聚合的关联文本分类方法[J]. 西安交通大学学报, 2012,46(12):6-11+122.

Associative Rule-Based Text Categorization Method Using Category Similarity[J]. 2012, 46(12): 6-11+122.
田丰 1, 桂小林 1, 杨攀 1, 等. 采用类别相似度聚合的关联文本分类方法[J]. 西安交通大学学报, 2012,46(12):6-11+122. DOI：

Associative Rule-Based Text Categorization Method Using Category Similarity[J]. 2012, 46(12): 6-11+122. DOI：

摘要

针对基于关联规则的分类方法在分类时仅考虑规则的置信度并使用规则修剪技术

导致分类器的分类精度难以进一步提高的问题

提出了一种基于类别相似度聚合的关联文本分类方法.该方法采用修改的χ

统计技术提取各类别的特征词; 为保证规则匹配的精度和速度

使用CR-tree存储分类规则

并给出了CR-tree的构建与匹配算法; 采用向量内积来计算文本类别分量与类别标志向量的相似度

进而使用规则置信度和类别相似度的聚合值作为文本分类的依据.基于实际网络文本的实验表明

该方法仅需提取30个特征词

分类结果的微平均值即可达到92.42%

优于未经剪枝的ARC-BC分类器及KNN、Bayes分类器; 在分类耗时方面

该方法与未经剪枝的ARC-BC分类器持平

表明该方法引入的相似度与聚合值的计算开销在可接受的范围内.

Abstract

Conventional association rule-based categorization methods have bottleneck in improving classifier's accuracy

since these methods only consider the rule confidence degree and use the pruning technique. A novel method to solve this problem is proposed

and is called associative rule-based classifier aggregating with category similarity(AACS). The method adopts the modified chi-square statistical technique to extract feature terms from each category

and employs the CR-tree to store classification rules. Algorithms to construct and to match CR-tree are proposed. Inner-product is used to calculate the similarity between the category sub vector of the text and the category feature vector

and then is aggregated with the rules' confidence degree to serve as the foundation of text categorization. Experimental results show that the method presented achieves a micro-average value of categorization 92.42% with extracting only 30 feature terms

which is better than the results of AWOPR

KNN

and Bayes classifiers. And the time complexity of the method is the same as that of AWOPR

indicating that the cost to calculate both the similarity and the aggregation is acceptable.

关键词

Keywords

references

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