Promotion Classification Using DecisionTree and Principal Component Analysis

Kaewwiset, Theeramet; Temdee, Punnarumol

doi:10.1109/ectidamtncon53731.2022.9720415

Cited by 5 publications

(1 citation statement)

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“…The experimental results showed the efficiency of this model. [26]. This paper proposed a new promotion system based on Principle Component Analysis (PCA) and machine learning classification techniques like the decision tree.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Application on Employee ‎Promotion

Ilwani,

Nassreddine,

Younis

2023

Mesopotamian Journal of Computer Science

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Any company's most valuable asset, its workforce, is its employees. As a result, the company's primary goal should be to develop an excellent strategy for supporting and investing in its employees and staff by providing the best training and development. Employee Promotion denotes the advancement of an employee's rank. It raises the salary, position, duties, and benefits. It is a part of the job that propels employees to the highest commitment and loyalty to their organizations. Employee morale and loyalty are two critical components of any successful business. Employee promotion is the key to improving employee performance and engagement. It is a complex process that may require effort and time from the human resource department. However, artificial intelligence is a recent science that has proven effective in many sectors, including healthcare and finance. Thus, this study used artificial intelligence techniques to automate employee promo. A new promotion approach based on machine learning techniques was proposed. Three machine learning techniques were applied: Logistic regression, support vector machine, and random forest. The result shows the efficacy of these techniques on the employee promotion problem.

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Section: Literature Reviewmentioning

confidence: 99%