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 ...

Methods, such as holdout, random subsampling, k-fold cross-validation, and bootstrap, for making error estimation are discussed. Also considered are general techniques, such as bagging and boosting, for increasing model accuracy. Directory • Table of

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 ...

background and objectives: soil organic carbon is a main soil property and particularly important for development and sustainable management of agricultural systems. soil organic matter content, which is typically measured in the form of soil organic carbon soc content, is commonly regarded as a key indicator of soil quality and utilization (liu et al., 2015). the presence of som has been prove...

Considering the case that prediction variable is a time series and response continuous scalar, we propose regression model based on improved PCA Bagging Algorithms. Compared with dimension reduction, proposed method uses distance correlation coefficient matrix instead of Person matrix, which makes distribution assumption original variables more free. an unsupervised reduction technique connecti...

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...

Bagging is a useful method to improve fruit quality by altering its exposure to light, whereas its effect on fruit volatiles production is inconsistent, and the genes responsible for the observed changes remain unknown. In the present study, single-layer yellow paper bags were used to study the effects of bagging treatment on the formation of C6 aldehydes in peach fruit (Prunus persica L. Batsc...