基于数据驱动的人力资源成本评估算法

摘要

针对当前人力资源成本评估算法存在准确度低、效果差等难题,为了提高人力资源成本评估精度,设计了基于数据驱动的人力资源成本评估算法.收集人力资源成本评估数据,并采用混沌理论对数据进行重构,还原人力资源成本变化特点,通过极限学习机建立人力资源成本评估算法,并通过粒子群算法对极限学习机进行优化,最后进行了人力资源成本评估仿真实验.结果表明,所提算法可以反映人力资源成本的变化特点,改善了人力资源成本的评估结果,获得了比其他人力资源成本评估模型更优的结果,具有广泛的应用前景.%Since the current human resource cost evaluation algorithm has the problems of low accuracy and poor effect,a human resource cost evaluation algorithm based on data driving is designed to improve the evaluation accuracy of human re?source cost. The data of human resource cost assessment is collected,and reconstructed with chaos theory to restore the change characteristics of human resource cost. The extreme learning machine is used to establish the human resource cost evaluation al?gorithm,and optimized with particle swarm optimization algorithm. The simulation experiment was performed for the human re?source cost assessment. The results show this algorithm can reflect the change characteristics of the human resource cost,im?prove the assessment results of human resource cost,obtain better results than other human resource cost evaluation models, and has wide application prospect.