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

• Published in: PeerJ. Computer science Vol. 10; p. e1875
• Main Authors: Li, Fanghong, Majid, Norliza Abdul, Ding, Shuo
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 22.02.2024; PeerJ, Inc; PeerJ Inc
• Subjects: Academic achievement; Accuracy; Algorithms; Artificial Intelligence; Career development; Career development planning; Careers; College graduates; Complexity; Computational linguistics; Data Mining and Machine Learning; Data Science; Decision trees; Deep learning; Gaussian mixture models; Higher education; Innovations; Integrated approach; Jeffreys prior; Kullback-Leibler; Labor market; Language processing; LSTM; Machine learning; Maximum likelihood estimation; Methods; Model accuracy; Natural language interfaces; Natural language processing; Neural networks; Personality; Personality traits; Prediction models; Probabilistic models; Regression analysis; Risk; Salary prediction; Skills; Social networks; Students; Success; Support vector machines; Wages; Wages & salaries; Maximum likelihood estimation; College graduate; LSTM; Kullback-Leibler; Gaussian mixture models; Salary prediction; Jeffreys prior
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1875

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.