基于机器学习的专业技术与技能质量测评模型优化分析  

Optimization analysis of professional technical and skill quality evaluation models based on machine learning

作　　者：高晓明 GAO Xiaoming(Guoneng Shuohuang Railway Development Co.,LTD.,Yuanping 034100,Shanxi China)

机构地区：[1]国能朔黄铁路发展有限责任公司,山西原平034100

出　　处：《粘接》2023年第6期140-143,共4页Adhesion

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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]