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 …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
A novel hierarchical selective ensemble classifier with bioinformatics application
Selective ensemble learning is a technique that selects a subset of diverse and accurate
basic models in order to generate stronger generalization ability. In this paper, we proposed …
basic models in order to generate stronger generalization ability. In this paper, we proposed …
Mechanism characteristic analysis and soft measuring method review for ball mill load based on mechanical vibration and acoustic signals in the grinding process
J Tang, J Qiao, Z Liu, X Zhou, G Yu, J Zhao - Minerals Engineering, 2018 - Elsevier
An operational optimization control for a mineral grinding process is limited by unmeasured
load parameter inside a ball mill given its complex and unclear production mechanism. A …
load parameter inside a ball mill given its complex and unclear production mechanism. A …
Cleaning noisy labels by negative ensemble learning for source-free unsupervised domain adaptation
Abstract Conventional Unsupervised Domain Adaptation (UDA) methods presume source
and target domain data to be simultaneously available during training. Such an assumption …
and target domain data to be simultaneously available during training. Such an assumption …
Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble
H Guo, L Chen, L Peng, G Chen - … of the 2016 ACM international joint …, 2016 - dl.acm.org
Effectively utilizing multimodal information (eg, heart rate and acceleration) is a promising
way to achieve wearable sensor based human activity recognition (HAR). In this paper, an …
way to achieve wearable sensor based human activity recognition (HAR). In this paper, an …
Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring
With the development of statistical methods and machine learning algorithms, credit scoring
is no longer a task merely based on experience. From single base classifiers to ensemble …
is no longer a task merely based on experience. From single base classifiers to ensemble …
Adaboost-stacking based on incremental broad learning system
Due to the advantages of fast training speed and competitive performance, Broad Learning
System (BLS) has been widely used for classification tasks across various domains …
System (BLS) has been widely used for classification tasks across various domains …
Combining forecasts: Performance and coherence
ME Thomson, AC Pollock, D Önkal… - International Journal of …, 2019 - Elsevier
There is general agreement in many forecasting contexts that combining individual
predictions leads to better final forecasts. However, the relative error reduction in a …
predictions leads to better final forecasts. However, the relative error reduction in a …
Metaheuristic-based ensemble learning: an extensive review of methods and applications
Ensemble learning has become a cornerstone in various classification and regression tasks,
leveraging its robust learning capacity across disciplines. However, the computational time …
leveraging its robust learning capacity across disciplines. However, the computational time …