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- W3039164170 abstract "Abstract Aiming at the problem of identifying the poor students in colleges, based on the improved classification imbalance, this paper proposes to establish the AdaBoost algorithm integrated learning model for the first time to solve the problem of identifying the poor students in colleges. In this model, decision tree is used as weak learner, and principal component analysis algorithm and balance cascade algorithm are used to reduce dimension and undersampling data. At the same time, the paper focuses on the comparison of the algorithms that choose the effect of sample set classification as the weight(SAMME.R) and the size of the prediction probability of sample set classification as the weight(SAMME). Experiments have proved that the AdaBoost ensemble learning algorithm model is better than the single classifier algorithm in predicting the results, which can ensure the fairness and objectivity of the recognition results, and has a strong application value for the recognition of poor students in colleges." @default.
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- W3039164170 date "2020-06-01" @default.
- W3039164170 modified "2023-09-23" @default.
- W3039164170 title "Prediction of Poor Students’ Classification Based on Adaboost Algorithm Integrated Learning Model" @default.
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- W3039164170 doi "https://doi.org/10.1088/1742-6596/1574/1/012172" @default.
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