Ensemble reinforcement learning: A survey
Reinforcement Learning (RL) has emerged as a highly effective technique for addressing
various scientific and applied problems. Despite its success, certain complex tasks remain …
various scientific and applied problems. Despite its success, certain complex tasks remain …
A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification
Sentiment analysis is a critical task of extracting subjective information from online text
documents. Ensemble learning can be employed to obtain more robust classification …
documents. Ensemble learning can be employed to obtain more robust classification …
[PDF][PDF] A taxonomy and short review of ensemble selection
Ensemble selection deals with the reduction of an ensemble of predictive models in order to
improve its efficiency and predictive performance. The last 10 years a large number of very …
improve its efficiency and predictive performance. The last 10 years a large number of very …
An analysis of ensemble pruning techniques based on ordered aggregation
G Martinez-Munoz… - IEEE Transactions on …, 2008 - ieeexplore.ieee.org
Several pruning strategies that can be used to reduce the size and increase the accuracy of
bagging ensembles are analyzed. These heuristics select subsets of complementary …
bagging ensembles are analyzed. These heuristics select subsets of complementary …
An ensemble pruning primer
Ensemble pruning deals with the reduction of an ensemble of predictive models in order to
improve its efficiency and predictive performance. The last 12 years a large number of …
improve its efficiency and predictive performance. The last 12 years a large number of …
[HTML][HTML] Explainable online ensemble of deep neural network pruning for time series forecasting
Both the complex and evolving nature of time series data make forecasting among one of
the most challenging tasks in machine learning. Typical methods for forecasting are …
the most challenging tasks in machine learning. Typical methods for forecasting are …
Focused ensemble selection: A diversity-based method for greedy ensemble selection
Ensemble selection deals with the reduction of an ensemble of predictive models in order to
improve its efficiency and predictive performance. A number of ensemble selection methods …
improve its efficiency and predictive performance. A number of ensemble selection methods …
Pruning an ensemble of classifiers via reinforcement learning
This paper studies the problem of pruning an ensemble of classifiers from a reinforcement
learning perspective. It contributes a new pruning approach that uses the Q-learning …
learning perspective. It contributes a new pruning approach that uses the Q-learning …
A selective multiclass support vector machine ensemble classifier for engineering surface classification using high definition metrology
S Du, C Liu, L Xi - Journal of Manufacturing Science …, 2015 - asmedigitalcollection.asme.org
The surface appearance is sensitive to change in the manufacturing process and is one of
the most important product quality characteristics. The classification of workpiece surface …
the most important product quality characteristics. The classification of workpiece surface …
Online ensemble aggregation using deep reinforcement learning for time series forecasting
A Saadallah, K Morik - 2021 IEEE 8th International Conference …, 2021 - ieeexplore.ieee.org
Both complex and evolving nature of time series structure make forecasting among one of
the most important and challenging tasks in time series analysis. Typical methods for …
the most important and challenging tasks in time series analysis. Typical methods for …