Bagging has been found to be successful in increasing the predictive performance of unstable classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then averages over all obtained classification rules. The idea of trimmed bagging is to exclude the bootstrapped classification rules that yield the highest error rates, as estimat...

Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distributions. In our new sampling method “Roughly Balanced Bagging” (RB Bagging), the number of samples in the largest and smallest classes are different, but they are effectively balanced when averaged over all subsets, wh...

Recently, the Entropy Ensemble Filter (EEF) method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging) method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (N...

If we look a few years back, we will find that ensemble classification model has outbreak many research and publication in the data mining community discussing how to combine models or model prediction with reduction in the error that results. When we ensemble the prediction of more than one classifier, more accurate and robust models are generated. We have convention that bagging, boosting wit...

We describe our method of traditional Phrase Structure Grammar (PSG) parsing in CIPS-Bakeoff2012 Task3. First, bagging is proposed to enhance the baseline performance of PSG parsing. Then we suggest exploiting another TreeBank (CTB7.0) to improve the performance further. Experimental results on the development data set demonstrate that bagging can boost the baseline F1 score from 81.33% to 84.4...

The paper compares different approaches to estimate the reliability of individual predictions in regression. We compare the sensitivity-based reliability estimates developed in our previous work with four approaches found in the literature: variance of bagged models, local cross-validation, density estimation, and local modeling. By combining pairs of individual estimates, we compose a combined...

Two learning ensemble methods, Bagging and Boosting, have been applied to decision trees to improve classification accuracy over that of a single decision tree learner. We introduce Bagging and propose a variant of it — Improved Bagging — which, in general, outperforms the original bagging algorithm. We experiment on 22 datasets from the UCI repository, with emphasis on the ensemble’s accuracy ...

Gradient Boosting and bagging applied to regressors can reduce the error due to bias and variance respectively. Alternatively, Stochastic Gradient Boosting (SGB) and Iterated Bagging (IB) attempt to simultaneously reduce the contribution of both bias and variance to error. We provide an extensive empirical analysis of these methods, along with two alternate bias-variance reduction approaches — ...

Bagging has been found to be successful in increasing the predictive performance of unstable classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then averages over all obtained classification rules. The idea of trimmed bagging is to exclude the bootstrapped classification rules that yield the highest error rates, as estimat...