改进的最大熵权值算法在文本分类中的应用

Research of Text Categorization Based on Improved Maximum Entropy Algorithm

由于传统算法存在着特征词不明确、分类结果有重叠、工作效率低的缺陷,为了解决上述问题,提出了一种改进的最大熵文本分类方法。最大熵模型可以综合观察到的各种相关或不相关的概率知识,对许多问题的处理都可以达到较好的结果。提出的方法充分结合了均值聚类和最大熵值算法的优点,算法首先以香农熵作为最大熵模型中的目标函数,简化分类器的表达形式,然后采用均值聚类算法对最优特征进行分类。经过实验论证,所提出的新算法能够在较短的时间内获得分类后得到的特征集,大大缩短了工作的时间,同时提高了工作的效率。

This paper discussed the problems in text categorization accuracy.In traditional text classification algorithm,different feature words have the same affecte on classification result,and classification accuracy is lower,causing the increase algorithm time complexity.Because the maximum entropy model can integrated various relevant or irrelevant probability knowledge observed,the processing of many issues can achieve better results.In order to solve the above problems,this paper proposed an improved maximum entropy text classification,which fully combines c-mean and maximum entropy algorithm advantages.The algorithm firstly takes shannon entropy as maximum entropy model of the objective function,simplifies classifier expression form,and then uses c-mean algorithm to classify the optimal feature.The simulation results show that the proposed method can quickly get the optimal classification feature subsets,greatly improve text classification accuracy,compared with the traditional text classification.