Strategies for Integrated Learning Approaches in Multi-Source Data Fusion
Yunzhen Wu · 2024
The multi-sourced data fusion approach becomes an important way to solve complex problems in the big data era, and it urgently needs discussion on how to fuse the diverse and heterogeneous data in effective ways. This paper is concerned with bank customers' loan data to discuss how to apply the ensemble learning method for forecasting the tendency of customers to buy loans. The paper first highlights that customer behaviour is complex and has manifold features, hence finding an appropriate fit can be hard using just one predictive model. An orchestrated learning approach to improvement of forecasting accuracy is combined in this study. Further, the implementation of several ensemble learning methods, such as Bagging, Boosting, and Stacking, is described by illustrating successful empirical experiments. It examines data on the bank customers' loans for performance evaluation of various ensemble learning methods to predict whether the customers will buy a loan. This paper compares advantages and disadvantages of those strategies; hence, it illustrates that ensemble learning methods give a significant improvement in the accuracy of loan purchase prediction. Boosting is the best algorithm presented since the subtlety of customer behavior is captured by this model, and improvement in performance can be really great. This research concludes with fresh insights about bank customer behavior prediction and data-driven support for the precision marketing strategies of banks.