Abstract—As the application of big data becomes more and more popular, machine learning algorithms are changing with each passing day, and the models produced by machine learning are increasingly diversified. The focus of big data applications has gradually shifted to the prediction and inference of models. How to choose the most suitable model for enterprise application scenarios among many machine learning models has become a topic of research that has attracted much attention. Ensemble methods have been proposed to discover best model by multiple training phase. Studies of finding best combination within multiple modes are still few. Configuring different machine learning models with appropriate parameters and looking for parameters is an NP-hard problem, which requires an optimization algorithm. This study proposes to apply differential evolution algorithm to integrate multiple trained machine learning models into an appropriate model. In this paper, the regression model is taken as an example and the differential evolution algorithm is compared with the particles swarm optimization algorithm. The results show that the differential evolution algorithm has better performance.
Index Terms—Big data, differential evolution, machine learning, optimization.
Yi-Chuan Chiu and Yung-Tsan Jou are with the Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan, Taiwan (e-mail: angel.chiu957@gmail.com, ytjou@cycu.edu.tw).
Hsing-Hung Lin is with Big Data Lab, Telecom. Laboratory Chunghwa Telecom. Co., Ltd., Taoyuan, Taiwan (e-mail: hsinhung@gmail.com).
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Cite: Yi-Chuan Chiu, Hsin-Hung Lin, and Yung-Tsan Jou, "Differential Evolution Based Model Selection Approach for Machine Learning," International Journal of Modeling and Optimization vol. 9, no. 3, pp. 135-139, 2019.
