Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions

Author:

Li Fanghong12,Majid Norliza Abdul1,Ding Shuo2

Affiliation:

1. Faculty of Human Development, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia

2. Guangxi University of Technology and Science, Liuzhou, Guangxi, China

Abstract

This article aims to address the challenge of predicting the salaries of college graduates, a subject of significant practical value in the fields of human resources and career planning. Traditional prediction models often overlook diverse influencing factors and complex data distributions, limiting the accuracy and reliability of their predictions. Against this backdrop, we propose a novel prediction model that integrates maximum likelihood estimation (MLE), Jeffreys priors, Kullback-Leibler risk function, and Gaussian mixture models to optimize LSTM models in deep learning. Compared to existing research, our approach has multiple innovations: First, we successfully improve the model’s predictive accuracy through the use of MLE. Second, we reduce the model’s complexity and enhance its interpretability by applying Jeffreys priors. Lastly, we employ the Kullback-Leibler risk function for model selection and optimization, while the Gaussian mixture models further refine the capture of complex characteristics of salary distribution. To validate the effectiveness and robustness of our model, we conducted experiments on two different datasets. The results show significant improvements in prediction accuracy, model complexity, and risk performance. This study not only provides an efficient and reliable tool for predicting the salaries of college graduates but also offers robust theoretical and empirical foundations for future research in this field.

Publisher

PeerJ

Reference29 articles.

1. Comparison between common statistical modeling techniques used in research, including: discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest;Abdulhafedh;Open Access Library Journal,2022

2. Career development impacts of COVID-19: practice and policy recommendations;Autin;Journal of Career Development,2020

3. Understanding information disclosure from secure computation output: a study of average salary computation;Baccarini,2022

4. Kullback-Leibler information as a basis for strong inference in ecological studies;Burnham;Wildlife Research,2001

5. Predicting students’ employability using support vector machine: a SMOTE-optimized machine learning system;Casuat;International Journal,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3