Human Resource Planning and Configuration Based on Machine Learning.

Enliang Dai,Qian Qi,Shuai Yuan,Yongfeng Liang,Baiyuan Ding

doi:10.1155/2022/3605722

Abstract

Human resources are the core resources of an enterprise, and the demand forecasting plays a vital role in the allocation and optimization of human resources. Starting from the basic concepts of human resource forecasting, this paper employs the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) to analyze human resource needs and determine the key elements of the company's human resource allocation through predictive models. With historical data as reference, the forecast value of current human resource demand is obtained through the two types of neural networks. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. In the experiment, the actual human resource data of a certain company are used as the experimental basic samples to train and test the two types of machine learning tools. The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources.

Highlights

Human resources are valuable corporate resources and are of great significance to their predictive analysis
Human resource demand forecasting generally needs to follow the principle of correlation and the principle of inertia. e principle of correlation is based on the correlation between the research objects and uses other objects to predict the targeted object [5–12]
The trend value of B and C can be predicted by suitable forecasting methods, and the correctness can be achieved by making predictions based on A. e principle of inertia refers to the slow progress of A or its regular development, and some valid past data can be obtained

Summary

Introduction

Human resources are valuable corporate resources and are of great significance to their predictive analysis. The trend value of B and C can be predicted by suitable forecasting methods, and the correctness can be achieved by making predictions based on A. e principle of inertia refers to the slow progress of A or its regular development, and some valid past data can be obtained. Under this premise, you can choose appropriate means to predict the trend value of A. e human resource demand forecasting model is mainly based on qualitative and quantitative analysis. The validity of the proposed method can be verified according to the experimental results

Machine Learning Models

Dataset and Comparison Method

Results and Analysis

Evaluation index