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Many workers at the production department of Libyan Textile Company work withdifferent performances. Plan of company management is paying the moneyaccording to the specific performance and quality requirements for each worker.Thus, it is important to predict the accurate evaluation of workers to extractthe knowledge for management, how much money it will pay as salary andincentive. For example, if the evaluation is average, then management of thecompany will pay part of the salary. If the evaluation is good, then it willpay full salary, moreover, if the evaluation is excellent, then it will paysalary plus incentive percentage. Twelve variables with 121 instances for eachvariable collected to predict the evaluation of the process for each worker.Before starting classification, feature selection used to predict theinfluential variables which impact the evaluation process. Then, fouralgorithms of decision trees used to predict the output and extract theinfluential relationship between inputs and output. To make sure get thehighest accuracy, ensemble algorithm (Bagging) used to deploy four algorithmsof decision trees and predict the highest prediction result 99.16%. Standarderrors for four algorithms were very small; this means that there is a strongrelationship between inputs (7 variables) and output (Evaluation). The curve of(Receiver operating characteristics) for algorithms gave a high-levelspecificity and sensitivity, and Gain charts were very close to together.According to the results, management of the company should take a logicdecision about the evaluation of production process and extract the importantvariables that impact the evaluation.

Use Bagging Algorithm to Improve Prediction Accuracy for Evaluation of Worker Performances at a Production Company