Richard. A. Olshen’s scientific contributions

... For quantitative evaluation, we compare our approach with six widely used methods for classification tasks on tabular data. These methods range from easily interpretable approaches such as RIPPER [15] and CART [16], which provide direct access to the rule used for a particular prediction, over less interpretable tree ensembles such as AdaBoost [17], Gradient Boosted Decision Trees (GBDT) [18] and Random Forests (RF) [19] up to a black-box MLP. Note that although tree ensembles offer some unique approaches in generating interpretations in the form of feature attributions [20], they are still often considered black-box approaches [21]. ...

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MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model

... On the other hand, the different condition ( ( , )/| |) represents the belonging to class . By separating different probabilities in the formula, different situations are evaluated in more detail (Breiman et al. 1984;Pal 2005). ...

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Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes

... The machine learning algorithms used in the experiment were Support Vector Classification (SVC) [49], k-Nearest Neighbor (k-NN) [50], Decision Tree (DT) [51], Random Forest (RF) [52], Artificial Neural Network (ANN) [53], Gradient Boosting Decision Tree (GBDT) [54], and TabNet [55]. These algorithms except for TabNet were implemented by scikit-learn [56], and the TabNet algorithm was implemented by the pytorch-tabnet library [57]. ...

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Self-Adaptive Traffic Anomaly Detection System for IoT Smart Home Environments

... Decision trees learn in three steps: feature selection, tree generation, and tree pruning (Saraswat 2022). ID3, C4.5, and CART provide the primary foundations for these steps (Quinlan 1979;Aaai/Iaai 1996;Breiman et al. 2017); as a result of using the DT model to estimate the burden of AMR, appropriate medical resources can be allocated (Naylor et al. 2017(Naylor et al. , 2018. According to Reynolds et al., reducing AMR or improving antibiotic selection can save healthcare utilization and costs (Reynolds et al. 2014). ...

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Artificial intelligence in drug resistance management