Ensemble learning is attracting much attention from pattern recognition and machine learning domains for good generalization. Both theoretical and experimental researches show that combining a set of accurate and diverse classifiers will lead to a powerful classification system. An algorithm, called FS-PP-EROS, for selective ensemble of rough subspaces is proposed in this paper. Rough set-based...

Neural network ensemble learning has been turned out to be an efficient strategy for achieving high performance, especially in fields where the development of a powerful single learning system requires considerable efforts (Lai et al., 2006, Yu et al., 2007, 2008b). Usually, neural network ensemble learning model outperforms the individual neural network models, whose performance is limited by ...

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemb...

Many situations in complex systems require quantitative estimates of the lack of information in one probability distribution relative to another. In short term climate and weather prediction, examples of these issues might involve the lack of information in the historical climate record compared with an ensemble prediction, or the lack of information in a particular Gaussian ensemble prediction...

At first the thesis introduces a new concept: coverage-based pattern mining. Then based on this concept, two ensemble pruning algorithms, CPM-EP and PMEP are put forward. By the algorithms we get candidate sub-patterns,and applies coverage pattern mining and a greedy strategy to acquire candidate target ensemble. Experiments show that pattern mining is an efficient selective integration strateg...

We present a new ensemble method that uses Entropy Guided Transformation Learning (ETL) as the base learner. The proposed approach, ETL Committee, combines the main ideas of Bagging and Random Subspaces. We also propose a strategy to include redundancy in transformation-based models. To evaluate the effectiveness of the ensemble method, we apply it to three Natural Language Processing tasks: Te...