基于改进贝叶斯的书目自动分类算法  

Improved Bayesian Algorithm Based Automatic Classification Method for Bibliography

作　　者：杨晓花[1] 高海云[2] YANG Xiao-hua;GAO Hai-yun(Zhicheng College,Fuzhou University,Fuzhou 350002,China;College of Physics and Information Engineering,Fuzhou University,Fuzhou 350016,China)

机构地区：[1]福州大学至诚学院,福州350002 [2]福州大学物理与信息工程学院,福州350016

出　　处：《计算机科学》2018年第8期203-207,共5页Computer Science

基　　金：福建省中青年教师教育科研项目(JAT160658)资助

摘　　要：贝叶斯算法被广泛应用于书目自动分类领域。该算法常使用差分进化算法来评估概率项,但是传统的差分进化算法容易陷入局部最优解,使得贝叶斯分类精度较低。针对该问题,提出了基于改进贝叶斯的书目自动分类方法。该方法通过多父突变和交叉操作估计概率项的最优解,提高贝叶斯分类精度;在进行书目自动分类时,先采用ICTCLAS系统进行文本预处理,再提取文本的词频-逆向文件频率特征,接着采用改进的贝叶斯估计方法对特征进行训练与分类,最终实现书目的自动分类。仿真结果表明,该方法具有较高的分类准确率。Bayesian algorithm is widely used in the field of automatic classification for bibliography.This method usually adopts differential evolution method to estimate the probability items.However,the traditional differential evolution method is easy to fall into the local optimum when estimating the probability items,which reduces the accuracy of Bayesian classifcation.In view of this problem,this paper proposed an improved Bayesian algorithm based automatic classification method for bibliography.In this method,the optimal solution of probability items is estimated through multi-parent mutation and crossover operation,which improves the accuracy of Bayesian classification.In the process of automatic classification for bibliography,the ICTCLAS system is used to preprocess the text and then extract the term frequency-inverse document frequency features of texts.Then,the improved Bayesian estimation method is utilized to train and classify the features.Finally,the automatic classification for bibliography is achieved.Simulation results show that this method has a high classification accuracy.

关 键 词：贝叶斯算法 书目自动分类 差分进化 特征提取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]