A Machine Learning Based Funding Project Evaluation Decision Prediction.

Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human mind. In this paper, we apply machine learning to the field of funding allocation decision making, and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision? and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding? Since funding data is characterized by imbalanced data distribution, we propose a slacked weighted entropy decision tree (SWE-DT). We assign weight to each class with the help of slacked factor. The experimental results show that the SWE decision tree performs well with sensitivity of 0.87, specificity of 0.85 and average accuracy of 0.75. It also provides a satisfied classification accuracy with Area Under Curve (AUC) = 0.87. This implies that the proposed method accurately classified minority class instances and suitable to imbalanced datasets. By adding evaluator factors into the model, sensitivity is improved by over 9%, specificity improved by nearly 8% and the average accuracy also increased by 7%. It proves the feasibility of using evaluators' characteristics as predictors. And by innovatively using machine learning method to predict evaluation decisions based on the personal characteristics of evaluators, it enriches the literature in the field of decision making and machine learning field. [ABSTRACT FROM AUTHOR]

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