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Toxicity of chemicals induced by different factors is an important consideration, especially during the drug research and development process. Thus, there is urgent need to develop computationally effective models that can predict the toxicity or adverse effects of chemicals for a specific class of chemicals. In this study, random forest (RF) was used to classify five toxicity data sets from Distributed...
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...
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