Random forest difficulty classification algorithm based on reinforcement learning
The working goal of student financial assistance is"full coverage and no omission of financial assistance for students from poor families",and it focuses on helping extremely poor students successfully complete their studies.Based on the smart campus platform,this paper proposes a classification algorithm for cost-sensitive students with difficulty based on reinforcement learning.The cost-sensitive characteristics of unbalanced data are introduced into the generation process of random forest,and the cumulative return coefficient of reinforcement learning is used to influence the selection of CART decision trees when the attributes are split,in order to achieve the effect of improving the overall classification accuracy of students with difficulties and the classification accuracy of students with special difficulties.The experimental results show that compared with the existing classification algorithms,the proposed algorithm is effective in both the overall classification of students with difficulty and the classification accuracy of students with extreme difficulty.
students from poor familiesrandom forestdeep learningcost sensitive
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