Dong-Sheng Cao, Yi-Zeng Liang, Qing-Song Xu, Liang-Xiao Zhang, Qian-Nan Hu and Hong-Dong Li Feature importance sampling-based adaptive random forest as a useful tool to screen underlying lead compounds Journal of Chemometrics 25
Good performance of ensemble approaches could generally be obtained when base classiﬁers are diverse and accurate. In the present study, feature importance sampling-based adaptive random forest (ﬁsaRF) was proposed to obtain superior classiﬁcation performance to the primal one-step random forest (RF). ﬁsaRF takes a convenient, yet very effective, way called feature importance sampling (FIS), to select the more eligible feature subset at each splitting node instead of simple random sampling and thereby strengthen the accuracy of individual trees, without sacriﬁcing diversity between them. Additionally, the iterative use of feature importance obtained by the previous step can adaptively capture the most signiﬁcant features in data and effectively deal with multiple classiﬁcation problems, not easily solved by other feature importance indexes.
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