A Combined Approach for Predicting Employees' Productivity based on Ensemble Machine Learning Methods

Garment industrial sector is one of the most important business sectors in the world. It presents the lifeblood for many countries' economy. The demanding of garment merchandise in accretion year over year. There are many key factors affecting the performance of this sector including the employees' productivity. This research proposes a hybrid approach which aims to predict the productivity performance of garment employees by combining different classification algorithms including J48, random forest (RF), Radial Base Function network (RBF), Multilayer Perceptron (MLP), Naive bayes (NB) and Support vector machine (SVM) with ensemble learning algorithms (Adaboost and bagging) on garment employees' productivity dataset. This work monitors three major evaluation metrics namely, accuracy, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results show that RF outperforms the other standard algorithms with accuracy of 0.983 and RSME of 0.1423. Applying Bagging and Adaboost with all standard classification algorithms on the dataset succeed in enhancing almost all classifiers' performance. Adaboost and bagging algorithms has been applied with all classification algorithms using different number of iterations starting from 1-100. The best result is achieved by applying Adaboost ensemble algorithm with J48 algorithm on its 20th iteration with an outstanding accuracy of 0.9916 and RSME of 0.0908.