Various approaches to extend bagging ensembles for class imbalanced data are considered. First, we review known extensions and compare them in a comprehensive experimental study. The results show that integrating bagging with under-sampling is more powerful than over-sampling. They also allow to distinguish Roughly Balanced Bagging as the most accurate extension. Then, we point out that complex...

Classifiers built on small training sets are usually biased or unstable. Different techniques exist to construct more stable classifiers. It is not clear which ones are good, and whether they really stabilize the classifier or just improve the performance. In this paper bagging (bootstrapping and aggregating (1)) is studied for a number of linear classifiers. A measure for the instability of cl...

In this paper we present an improvement of the precision of classification algorithm results. Two various approaches are known: bagging and boosting. This paper describes a set of experiments with bagging and boosting methods. Our use of these methods aims at classification algorithms generating decision trees. Results of performance tests focused on the use of the bagging and boosting methods ...

Bagging is one of the older, simpler and better known ensemble methods. However, the bootstrap sampling strategy in bagging appears to lead to ensembles of low diversity and accuracy compared with other ensemble methods. In this paper, a new variant of bagging, named IGF-Bagging, is proposed. Firstly, this method obtains bootstrap instances. Then, it employs Information Gain (IG) based feature ...

Tomato borers, especially Tuta absoluta (Lepidoptera: Gelechiidae), a pest introduced in southern Europe, northern Africa and the Middle East, and diseases can damage tomato (Solanum lycopersicum) fruit. This study tested the economic and technical feasibility of bagging tomato fruits clusters during organic production to protect them against insects and diseases. The experiment was randomized ...

In the regression context, boosting and bagging are techniques to build a committee of regressors that may be superior to a single regressor. We use regression trees as fundamental building blocks in bagging committee machines and boosting committee machines. Performance is analyzed on three non-linear functions and the Boston housing database. In all cases, boosting is at least equivalent, and...

Bagging is a popular ensemble algorithm based on the idea of data resampling. In this paper, aiming at increasing the incurred levels of ensemble diversity, we present an evolutionary approach for optimally designing Bagging models composed of heterogeneous components. To assess its potentials, experiments with well-known learning algorithms and classification datasets are discussed whereby the...

Often, relations between economic variables cannot be exploited for forecasting, suggesting that predictors are weak in the sense estimation uncertainty is larger than bias from ignoring relation. In this paper, we propose a novel bagging estimator designed such predictors. Based on test finite-sample predictive ability, our shrinks ordinary least squares estimate—not to zero, but towards null ...

We present a visual tablet for exploring the nature of a bagged decision tree (Breiman [1996]). Aggregating classifiers over bootstrap datasets (bagging) can result in greatly improved prediction accuracy. Bagging is motivated as a variance reduction technique, but it is considered a black box with respect to interpretation. Current research seekine: to explain why bagging works has focused ond...