Ensemble Learning Boosting Model of Improving Classification and Predicting - Bambang, Siswoyo A; Nanna, Suryana B; DA, Dewi C* (INTI International University, 2020)
Artificial Intelligence Engineering is an important topic and has been studied extensively in various fields. Machine learning is part of Artificial Intelligence that has been used to solve prediction problems and financial decision making. An effective prediction model is one that can provide a higher prediction accurate, that is the goal of prediction model development. In the previous literature, various classification techniques have been developed and studied, which by combining several classifier approaches have shown performance over a single classifier. In building a boosting ensemble model, there are three critical issues that can affect model performance. First are the classification techniques actually used; the second is a combination method for combining several classifiers; and all three classifiers to be combined. This paper conducts a comprehensive study comparing the ensemble boosting classifier and three widely used classification techniques including AdaBoost, Gradient boosting, XGB Classifier. The results of the experiment with two financial ratio datasets show that the Ensemble Boosting Classifier has the best performance with an accurate value of 98%, while AdaBoost is 96%, Gradient_boosting is 98%, and XGB Classifier is 98%. Ensemble Boosting matches all available data, so the predict () function can be called to make predictions on new data.