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 is a method of obtaining more robust predictions when the model class under consideration is unstable with respect to the data, i.e., small changes in the data can cause the predicted values to change significantly. In this paper, we introduce a Bayesian version of bagging based on the Bayesian bootstrap. The Bayesian bootstrap resolves a theoretical problem with ordinary bagging and of...

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...

This paper describes a set of experiments with bagging – a method, which can improve results of classification algorithms. Our use of this method aims at classification algorithms generating decision trees. Results of performance tests focused on the use of the bagging method on binary decision trees are presented. The minimum number of decision trees, which enables an improvement of the classi...

Ensembles of classifiers are among the strongest classifiers in most data mining applications. Bagging ensembles exploit the instability of base-classifiers by training them on different bootstrap replicates. It has been shown that Bagging instable classifiers, such as decision trees, yield generally good results, whereas bagging stable classifiers, such as k-NN, makes little difference. Howeve...

Riassunto: Il Bagging è una tecnica di aggregazione, in cui uno stimatore viene ottenuto come media di predittori calcolati su campioni bootstrap. Gli alberi di decisione con il bagging quasi sempre migliorano il predittore originario, ed è opinione comune che l’efficacia del bagging sia dovuta alla riduzione della varianza. In questo lavoro mostriamo un contro-esempio e diamo evidenza sperimen...