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...

In this paper, a new variant of Bagging named DepenBag is proposed. This algorithm obtains bootstrap samples at first. Then, it employs a causal discoverer to induce from each sample a dependency model expressed as a Directed Acyclic Graph (DAG). The attributes without connections to the class attribute in all the DAGs are then removed. Finally, a component learner is trained from each of the r...

As growing numbers of real world applications involve imbalanced class distribution or unequal costs for misclassification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of c...

In this paper, we apply the combination method of bagging which has been developed in the context of supervised learning of classifiers and regressors to the unsupervised artificial neural network known as the Self Organising Map. We show that various initialisation techniques can be used to create maps which are comparable by humans by eye. We then use a semi-supervised version of the SOM to c...