Reinforcement Learning (RL) has emerged as a highly effective technique for addressing
various scientific and applied problems. Despite its success, certain complex tasks remain …

Sentiment analysis is a critical task of extracting subjective information from online text
documents. Ensemble learning can be employed to obtain more robust classification …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …