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A global solution derived from a weighted linear way improves the prediction with
desirable results in multi-agent systems (MAS). In this paper, we provide two novel
contributions. First, we propose a linear pooling approach to merge data from
multiple agents which outperforms to standard single Machine Learning (ML)
algorithms. This approach can be applied for common agents’ target variable with
different agents’ descriptive variables. Second, we also examine the proposed method
versus various ML techniques with different splits and compared them in terms of a
standard measure of performance i.e. root mean square error for each agent with
global RMSE and noticed robust results. The proposed approach is implemented into
a real-world dataset (superstore) for two agents and compared to the standard ML
techniques. The results show that our approach significantly outperforms the
available ML methods.
