首页|University Teknologi Malaysia Researchers Publish New Study Findings on Machine Learning (Simplified Novel Approach for Accurate Employee Churn Categorization u sing MCDM, De-Pareto Principle Approach, and Machine Learning)
University Teknologi Malaysia Researchers Publish New Study Findings on Machine Learning (Simplified Novel Approach for Accurate Employee Churn Categorization u sing MCDM, De-Pareto Principle Approach, and Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence. According to news originating from the University Teknologi Malaysia by NewsRx correspondents, research stated, "Churning of employees from organiza tions is a serious problem. Turnover or churn of employees within an organizatio n needs to be solved since it has negative impact on the organization." The news reporters obtained a quote from the research from University Teknologi Malaysia: "Manual detection of employee churn is quite difficult, so machine lea rning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, o nly one study looks into the categorization of employees up to date. A novel mul ti-criterion decision-making approach (MCDM) coupled with DE-PARETO principle ha s been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-st age MCDM scheme for categorizing employees. In 1st stage, analytic hierarchy pro cess (AHP) has been utilized for assigning relative weights for employee accompl ishment factors. In second stage, TOPSIS has been used for expressing significan ce of employees for performing employee categorization. A simple 20-30-50 rule i n DE PARETO principle has been applied to categorize employees into three major groups namely enthusiastic, behavioral and distressed employees."
University Teknologi MalaysiaAlgorithm sCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
