A survey on ensemble learning
Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …
learning methods may fail to obtain satisfactory performances when dealing with complex …
An overview of state-of-the-art partial discharge analysis techniques for condition monitoring
As one step toward the future smart grid, condition monitoring is important to facilitate the
reliability of grid asset operation and to save on maintenance cost [1]. Most failures of the …
reliability of grid asset operation and to save on maintenance cost [1]. Most failures of the …
集成学习方法: 研究综述
徐继伟， 杨云 - 云南大学学报(自然科学版), 2018 - yndxxb.ynu.edu.cn
机器学习的求解过程可以看作是在假设空间中搜索一个具有强泛化能力和高鲁棒性的学习模型,
而在假设空间中寻找合适模型的过程是较为困难的. 然而, 集成学习作为一类组合优化的学习 …
而在假设空间中寻找合适模型的过程是较为困难的. 然而, 集成学习作为一类组合优化的学习 …
Incremental semi-supervised clustering ensemble for high dimensional data clustering
Traditional cluster ensemble approaches have three limitations:() They do not make use of
prior knowledge of the datasets given by experts.() Most of the conventional cluster …
prior knowledge of the datasets given by experts.() Most of the conventional cluster …
Robust ensemble clustering using probability trajectories
Although many successful ensemble clustering approaches have been developed in recent
years, there are still two limitations to most of the existing approaches. First, they mostly …
years, there are still two limitations to most of the existing approaches. First, they mostly …
Two-stage selective ensemble of CNN via deep tree training for medical image classification
Medical image classification is an important task in computer-aided diagnosis systems. Its
performance is critically determined by the descriptiveness and discriminative power of …
performance is critically determined by the descriptiveness and discriminative power of …
[HTML][HTML] Clustering ensemble based on sample's stability
The objective of clustering ensemble is to find the underlying structure of data based on a
set of clustering results. It has been observed that the samples can change between clusters …
set of clustering results. It has been observed that the samples can change between clusters …
A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters
Cluster ensemble is an important research content of ensemble learning, which is used to
aggregate several base clusterings to generate a single output clustering with improved …
aggregate several base clusterings to generate a single output clustering with improved …
[HTML][HTML] A novel bagging C4. 5 algorithm based on wrapper feature selection for supporting wise clinical decision making
From the perspective of clinical decision-making in a Medical IoT-based healthcare system,
achieving effective and efficient analysis of long-term health data for supporting wise clinical …
achieving effective and efficient analysis of long-term health data for supporting wise clinical …
Automatic identification of the number of clusters in hierarchical clustering
Hierarchical clustering is one of the most suitable tools to discover the underlying true
structure of a dataset in the case of unsupervised learning where the ground truth is …
structure of a dataset in the case of unsupervised learning where the ground truth is …