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- W2602458379 abstract "Variable selection is very important for interpretation and prediction, especially for high dimensional datasets. In this paper, a new method is proposed based on Random Forest (RF) to select variables using Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG). We also use dichotomy method to screen variables, which is proved to perform very fast. Experiments on 10 microarray datasets show that the new method is proficient and robust. In addition, we compared the proposed method with other variable selection methods, and the results demonstrated that our proposed method is more robust and more powerful in both accuracy and CPU time." @default.
- W2602458379 created "2017-04-07" @default.
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- W2602458379 date "2016-08-01" @default.
- W2602458379 modified "2023-10-06" @default.
- W2602458379 title "Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest" @default.
- W2602458379 cites W2155261478 @default.
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- W2602458379 doi "https://doi.org/10.1109/icsess.2016.7883053" @default.
- W2602458379 hasPublicationYear "2016" @default.
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