文本作者分类分析中关键词子集的遗传优化
Genetic Optimization of Keywords Subset in the Classification Analysis of Texts Authorship
摘要：
分析了以文本频率为属性的关键词集的遗传选择问题。遗传优化是在一组单词上进行的，该组单词是具有给定频率限制的频率字典的分数。频率词典是在分析英语小说文本的文本序列的基础上形成的。采用最近k近邻分类器的误差作为适应度函数，通过遗传算法使适应度函数最小化。实验结果表明，基于遗传算法从频域词典中选择关键词集的属性，按作者类别进行文本分类具有较高的查准率和查全率。
英文摘要：
The genetic selection of keywords set, the text frequencies of which are considered as attributes in text classification analysis, has been analyzed. The genetic optimization was performed on a set of words, which is the fraction of the frequency dictionary with given frequency limits. The frequency dictionary was formed on the basis of analyzed text array of texts of English fiction. As the fitness function which is minimized by the genetic algorithm, the error of nearest k neighbors classifier was used. The obtained results show high precision and recall of texts classification by authorship categories on the basis of attributes of keywords set which were selected by the genetic algorithm from the frequency dictionary.
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。
