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Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Computers, materials & Continua 2022年第8期72卷 3985-3997页

作者： rabab hamed m.aly Aziza I.Hussein Kamel H.Rahouma The Higher Institute for management and Information Technology Minya61768Egypt Department of Electrical and Computer Engineering Effat UniversityJeddahKSA Electrical Engineering Department Faculty of EngineeringMinia UniversityMinia6111Egypt

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’*** algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best *** work of this paper consists of two *** first part is based on collecting data of employees to calculate and illustrate the performance of each *** second part is based on the classification and prediction techniques of the employee *** model is designed to help companies in their decisions about the employees’*** classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the *** of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming *** also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high *** proposed algorithm is compared with other known techniques where our results are fund to be superior.

关键词： metaheuristic algorithm KUWAHARA filter Grasshopper optimization algorithm and Gradient boosting tree

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