Chuqing Zhang1, Jiangyuan Yao2,*, Guangwu Hu3, Xingcan Cao4
Computer Systems Science and Engineering, Vol.45, No.2, pp. 2111-2124, 2023, DOI:10.32604/csse.2023.030516
Abstract 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… More >
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