Using Hybrid and Diversity-Based Adaptive Ensemble Method for Binary Classification

Abstract

This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adaptive selection of the most suitable model for each data instance. Ensemble method, an important machine learning technique uses multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. In a given dataset the application of diverse single models trained with different machine learning algorithms will have different capabilities in recognizing patterns in the given training sample. The proposed approach has been validated on Repeat Buyers Prediction dataset and Census Income Prediction dataset. The experiment results indicate up to 18.5% improvement on F1 score for the Repeat Buyers dataset compared to the best individual model. This improvement also indicates that the proposed ensemble method has an exceptional ability of dealing with imbalanced datasets. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score. Finally, our results produced a slightly higher AUC score of 0.718 compared to the previous result of AUC score of 0.712 in the Repeat Buyers competition. This roughly 1% increase AUC score in performance is significant considering a very big dataset such as Repeat Buyers.