Class imbalance presents a major hurdle in the application of data mining methods. A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data. For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique (SMOTE). However, most of the resampling methods entail asymmetric changes t...

Bagging is one of the most effective computationally intensive procedures to improve on unstable estimators or classifiers, useful especially for high dimensional data set problems. Here we formalize the notion of instability and derive theoretical results to analyze the variance reduction effect of bagging (or variants thereof) in mainly hard decision problems, which include estimation after t...

Bagging forms a committee of classijiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, disjoint partitions result in betterperformance than bootstrap aggregates (bags). Many applications (e.g., protein struc...

The incidence of Dupuytren's contracture in a polyvinyl chloride (PVC) manufacturing plant, where a great deal of bagging and packing took place by hand, was higher than in another plant in which there was no bagging or packing. The incidence in the packing plant was double that found in an earlier survey by Early at Crewe Locomotive Works of 4801 individuals, most of whom were manual workers. ...

In the paper the investigation of m-out-of-n bagging with and without replacement using genetic neural networks is presented. The study was conducted with a newly developed system in Matlab to generate and test hybrid and multiple models of computational intelligence using different resampling methods. All experiments were conducted with real-world data derived from a cadastral system and regis...

This paper proposes an approach to improve statistical word alignment with ensemble methods. Two ensemble methods are investigated: bagging and cross-validation committees. On these two methods, both weighted voting and unweighted voting are compared under the word alignment task. In addition, we analyze the effect of different sizes of training sets on the bagging method. Experimental results ...

Bagging, boosting and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-regressor. In this work, we built an ensemble of bagging, boosting and random subspace methods ensembles with 8 sub-regressors in each one and then an averaging methodology is used for the final prediction. We ...

Combining machine learning models is a means of improving overall accuracy. Various algorithms have been proposed to create aggregate models from other models, and two popular examples for classification are Bagging and AdaBoost. In this paper we examine their adaptation to regression, and benchmark them on synthetic and real-world data. Our experiments reveal that different types of AdaBoost a...