一种基于改进ECCD和类别权重的文档分类系统

Chungseok Han, Sang-Yong Park, Soowon Lee
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引用次数: 4

摘要

Web信息服务需要一个文档分类系统来实现高效的管理和方便的检索。现有的文档分类系统存在分类准确率低的问题,如果在文档中选择的特征词数量很少,或者属于特定类别的文档数量过大。为了解决这一问题,我们提出了一种使用“改进ECCD”特征选择方法和“每个文档的类别权重”的文档分类系统。实验结果表明,“改进的ECCD”特征选择方法在分类上具有更高的准确率。此外,将“每个文档的类别权重”特征值与“改进的ECCD”特征选择方法相结合,可以提高分类的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Document Classification System Using Modified ECCD and Category Weight for each Document
Web information service needs a document classification system for efficient management and conveniently searches. Existing document classification systems have a problem of low accuracy in classification, if a few number of feature words is selected in documents or if the number of documents that belong to a specific category is excessively large. To solve this problem, we propose a document classification system using `Modified ECCD` feature selection method and `Category Weight for each Document`. Experimental results show that the `Modified ECCD` feature selection method has higher accuracy in classification than and the ECCD method. Moreover, combining the `Category Weight for each Document` feature value and `Modified ECCD` feature selection method results better accuracy in classification.
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