Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

Abstract

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The 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 performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.