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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

Article first published online: 17 FEB 2011 | DOI: 10.1002/cem.1375

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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