摘要
技能质量关系到企业的核心竞争力,是企业转型升级的关键,构建基于机器学习的企业技能质量评价体系模型,提升企业技能质量评价准确性。以机器学习常用算法支持向量机(SVM)来设计企业技能质量评价体系模型,并采用改进天牛须搜索算法(BAS)对SVM参数优化,提出改进的BAS-SVM企业技能质量评价体系模型。对比该模型和SVM模型、BAS-SVM模型、层次分析法模型,结果表明,改进BAS-SVM企业技能质量评价体系模型的评价准确率高达94.8%,且具有良好的鲁棒性。
The quality of enterprise skills is related to the core competitiveness of enterprises and is the key to enterprise transformation and upgrading.Constructing a machine learning based evaluation system model for enterprise skill quality can improve the accuracy of enterprise skill quality evaluation.Design an enterprise skill quality evaluation system model using the commonly used machine learning algorithm Support Vector Machine(SVM),and optimize SVM parameters using the improved Beetle Antennae search algorithm(BAS)to propose an improved BAS-SVM enterprise skill quality evaluation system model.Comparing this model with SVM model,BAS-SVM model,and Analytic Hierarchy Process model,the results showed that the improved BAS-SVM enterprise skill quality evaluation system model had a high evaluation accuracy of 94.8%and good robustness.
作者
高晓明
GAO Xiaoming(Guoneng Shuohuang Railway Development Co.,LTD.,Yuanping 034100,Shanxi China)
出处
《粘接》
CAS
2023年第6期140-143,共4页
Adhesion
关键词
天牛须搜索算法
机器学习
技能质量评价体系
层次分析法
化工企业
beetle antennae search algorithm
machine learning
skill quality evaluation system
analytic hierarchy process
chemical enterprise
