Ensemble learningRandom forest/extra treesAs a common screening and diagnostic tool, Fine Needle Aspiration Biopsy (FNAB) of the suspicious breast lumps can be used to distinguish between malignant and benign b
Real-time monitoring radiofrequency ablation using tree-based ensemble learning modelsRadiofrequencyablationtumorcancercontrolmonitoringmachinelearningensemblelesionBackground: Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current...
1, the ensemble model lacks interpretability similar to the deep learning model. In contrast, linear and tree-based models have superior interpretability, but their accuracy is generally insufficient. Therefore, the development of a machine learning model that achieves both accuracy and interpretability ...
In particular, ensemble learning algorithms such as Random Forest are well suited for large datasets such as that of amperometry data and can easily adapt to non-linearities found in the data [2,3]. Using the trained classifier, new unlabeled data are passed to predict the label. Based on ...
www.nature.com/scientificreports OPEN Decision tree based ensemble machine learning model for the prediction of Zika virus T‑cell epitopes as potential vaccine candidates Syed Nisar Hussain Bukhari1, Julian Webber2 & Abolfazl Mehbodniya2* Zika fever is an infectious disease...
其实这里也可以看做事一种高级的ensemble learning/random forest,集成学习中,在组合基模型的预测结果时,一般是用投票法或者平均值法,而这里可以看做是用神经网络组合基模型的预测结果,此时的基模型是决策树,也可以说是随机森林做了个改进。类似于集成学习中的stack方法,将基模型的预测结果输入一个预测模型再学习一次...
This study analyzes PCA patient data and conducts several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results confirm the feasibility of the proposed ensemble approach to postoperative pain management....
You’ll also learn to use boosted trees, a powerful machine learning technique that uses ensemble learning to build high-performing predictive models. Along the way, you'll work with health and credit risk data to predict the incidence of diabetes and customer churn.Lire la suite Conditions ...
learning (artificial intelligence)pattern clusteringsampling methods/ data miningtree-based ensemble techniquesdecision treesLarge amounts of data from high-throughput analytical instruments have generally become more and more complex, bringing a number of challenges to statistical modeling. To understand ...
As a result, this study proposes a tree-based ensemble learning framework known as the SPT, which combines the data partition and space combination. The proposed SPT is quite different from the existing learning methods in terms of the special features, listed as follows: The remainder of this...