This paper presents an adaptive method to search best number of trees (Ntree) in complete random forest (CRF). Ada- CRF can automatically determine whether the forest has reached a stable state during the establishment of the forest, thereby avoiding inaccurate results caused by too small Ntree, or low efficiency caused by too large Ntree. As a general sampling method, Ada-CRF can not only effectively compress the amount of data, but also filter label noise to improve data quality. Ada-CRF can identify the noise points in the data by automatically searching for the results of the complete random tree division of the data. To improve the data quality, it filters the noise points of the label and retains valid data.
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