摘要
提出基于朴素贝叶斯算法的电网建设人力资源自动化分类模型研究。通过网络爬虫技术,采集电网建设资源,在采集的资源信息中提取资源特征,并应用随机森林算法在特征项中选取合适的特征项,生成特征子集。采用朴素贝叶斯算法,构建基于属性相关性度量的分类模型,实现电网建设人力资源自动化分类。结果表明,在两分类和多分类条件下,文中提出的分类模型的平均适应度值分别为89.78和97.47,该分类模型能够获取准确的自动化分类结果,提高模型的适应度值,满足电网建设需求。
The automatic classification model of power grid construction resources based on naive Bayesian algorithm is proposed.The network crawler technology is used to collect the power grid construction resources,and the resource features are extracted from the collected resource information.In addition,the random forest algorithm is applied to select the appropriate feature items,which are used to generate the feature subset.The naive Bayes algorithm is adopted to construct a classification model based on attribute correlation measurement.Thus the automatic classification of human resources in power grid construction is realize.The results show that under two classification and multiple classification conditions,the average fitness of the proposed classification model is 89.78 and 97.47,respectively.The classification model can obtain accurate automatic classification results,improve the fitness of the model,and meet the requirements of power grid construction.
作者
夏常明
Xia Changming(State Grid Gansu Electric Power Company,Lanzhou 730030,China)
出处
《粘接》
CAS
2021年第12期93-97,共5页
Adhesion
关键词
朴素贝叶斯算法
电网建设
自动化分类模型
特征属性
Naive Bayes algorithm
Power grid construction
Automatic classification model
Feature attributes
