When tuned for a certain condition, the model becomes biased to the data used for training limiting the model's generalisation ability.In this paper, we propose a BIC-based tuning-free approach for speaker segmentation through the use of ensemble-based learning. A forest of segmentation trees ...
All these models are tree-based ensemble learning algorithms. The ensemble method integrates the predictions of multiple weak classifiers to generate a strong classifier, which has the advantages of improving prediction performance, reducing errors, and enhancing the generalization ability of the models. ...
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 ...
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 ...
We report on applications of these algorithms to variable selection, outlier detection, supervised pattern analysis, cluster analysis, and tree-based kernel and ensemble learning. Through this report, we wish to inspire chemists to take greater interest in decision trees and to obtain greater benefits...
The random forest (RF) technique was employed to rank the features in the dataset under investigation on the basis of their importance concerning amebiasis. Several decision trees were built using the robust ensemble learning methodology known as the RF method, and their outputs were combined to in...
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...
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...