Text Classification Based on Central Vector Method and Universal Gravitation

WANG Yu; LIU Bin

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Text Classification Based on Central Vector Method and Universal Gravitation

更新时间：2026-09-04

- Text Classification Based on Central Vector Method and Universal Gravitation
- Vol. 34, Issue 11, Pages: 119-123(2017)
- 作者机构：
  
  南京工业大学机构计算机科学与技术学院,江苏,南京,211816
- 作者简介：
- 基金信息：
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- Published：2017
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王宇, 刘斌. 基于中心向量法与万有引力模型相结合的文本分类[J]. 微电子学与计算机, 2017, 34(11): 119-123.

WANG Yu, LIU Bin. Text Classification Based on Central Vector Method and Universal Gravitation[J]. 2017, 34(11): 119-123.
王宇, 刘斌. 基于中心向量法与万有引力模型相结合的文本分类[J]. 微电子学与计算机, 2017, 34(11): 119-123. DOI：

WANG Yu, LIU Bin. Text Classification Based on Central Vector Method and Universal Gravitation[J]. 2017, 34(11): 119-123. DOI：

摘要

提出一种中心向量模型与万有引力模型相结合的文本分类的方法(CGMV).引入了类内信息熵和类间边界置信度

解决了传统模型下类间分布不均匀与类内个别样本分布稀疏从而导致分类准确度下降的问题.经过实验证明CGMV算法提高了文本分类的准确率.

Abstract

This paper presents a text categorization method (CGMV) that combines the central vector model with the gravitation mode. The intra-class information entropy and inter-class boundary confidence degree are introduced to solve the problem that the classification accuracy is degraded due to the heterogeneity of inter-class distribution and the sparse distribution of individual samples in the traditional model. Experiments show that CGMV algorithm improves the efficiency of text categorization.

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references

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