基于朴素贝叶斯分类器和遗传算法的关联规则文本分类
Text Classification using Association Rule with a Hybrid Concept of Naive Bayes Classifier and Genetic Algorithm
摘要：
文本分类是将自然语言文本根据其内容自动分配到预定义的类别。文本分类是文本检索系统和文本理解系统的主要要求，文本检索系统根据用户查询检索文本，文本理解系统以某种方式转换文本，例如生成摘要，回答问题或提取数据。文本分类的需求日益增长。考虑到这一需求，正在开发新的和更新的技术，用于自动文本分类的目的。本文提出了一种新的文本分类算法。该方法不使用词，而是使用词关系即关联规则从预先分类的文本文档中提取特征集。然后将朴素贝叶斯分类器的概念用于派生特征，最后添加了遗传算法的概念以进行最终分类。一个系统的基础上所提出的算法已经实施和测试。实验结果表明，该系统是一个成功的文本分类器。
英文摘要：
Text classification is the automated assignment of natural language texts to predefined categories based on their content. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Now a day the demand of text classification is increasing tremendously. Keeping this demand into consideration, new and updated techniques are being developed for the purpose of automated text classification. This paper presents a new algorithm for text classification. Instead of using words, word relation i.e. association rules is used to derive feature set from pre-classified text documents. The concept of Naive Bayes Classifier is then used on derived features and finally a concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
