Bagging is a device intended for reducing the prediction error of learning algorithms. In its simplest form, bagging draws bootstrap samples from the training sample, applies the learning algorithm to each bootstrap sample, and then averages the resulting prediction rules. We extend the definition of bagging from statistics to statistical functionals and study the von Mises expansion of bagged ...

The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochasti...

The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate bootstrap samples of equal size as the original training set mwor = n. Without-replacement methods typically use half samples mwr = n/2. These choices of sampling sizes are arbitrary and need not be optimal in terms of...

Boosting and bagging are two techniques for improving the performance of learning algorithms. Both techniques have been successfully used in machine learning to improve the performance of classification algorithms such as decision trees, neural networks. In this paper, we focus on the use of feedforward back propagation neural networks for time series classification problems. We apply boosting ...

Bagging is a simple and robust classification algorithm in the presence of class label noise. This algorithm builds an ensemble of classifiers by bootstrapping samples with replacement of size equal to the original training set. However, several studies have shown that this choice of sampling size is arbitrary in terms of generalization performance of the ensemble. In this study we discuss how ...

Recent work has shown that combining multiple versions of unstable classifiers such as trees or neural nets results in reduced test set error. To study this, the concepts of bias and variance of a classifier are defined. Unstable classifiers can have universally low bias. Their problem is high variance. Combining multiple versions is a variance reducing device. One of the most effective is bagg...

Many researchers have shown that ensemble methods such as Boosting and Bagging improve the accuracy of classification. Boosting and Bagging perform well with unstable learning algorithms such as neural networks or decision trees. Pruning decision tree classifiers is intended to make trees simpler and more comprehensible and avoid over-fitting. However it is known that pruning individual classif...

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...

In combining classifiers, it is believed that diverse ensembles perform better than non-diverse ones. In order to test this hypothesis, we study the accuracy and diversity of ensembles obtained in bagging and boosting applied to the nearest mean classifier. In our simulation study we consider two diversity measures: the Q statistic and the disagreement measure. The experiments, carried out on f...