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In this research, Bagging algorithm that incorporates different classifier into classifier ensembles models for pixel classification is suggested. We chose classifier ensembles with decision trees, as the base classifiers. In the problem of pixel classification, experimental results demonstrate the effectiveness of the Bagging with forest of random trees (RandomForest) as base classifier compared...
Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation...
In this paper, a new method for question classification is proposed, which employs ensemble learning algorithms to train multiple question classifiers. These component learners are combined to produce the final hypothesis. In detail, the feature spaces are obtained through extracting high-frequency keywords from questions corpus and the method of word semantic similarity is performed to adjust the...
In this study, we applied ensemble machine learning to evaluate credit scoring. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using all 14 features, using selected 6 features on Australian credit data form UCI data set. Results showed that in experiments with all features improved performance...
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