Please cite this article in press as: J. Abellán, E (2012), http://dx.doi.org/10.1016/j.inffus.2012.0 In this paper, we present an experimental comparison among different strategies for combining decision trees built by means of imprecise probabilities and uncertainty measures. It has been proven that the combination or fusion of the information obtained from several classifiers can improve the...

This paper investigates an ensemble feature selection algorithm that is based on genetic algorithms. The task of ensemble feature selection is harder than traditional feature selection in that one not only needs to find features germane to the learning task and learning algorithm, but one also needs to find a set of feature subsets that will promote disagreement among the ensemble’s classifiers...

This paper work is focused on the comparison of different data mining techniques and their performances by building predictive models of forest stand properties from satellite images. We used the WEKA data mining environment to implement our numeric prediction experiments, applying linear regression, model (regression) trees, and bagging. The best results (with regard to correlation) we obtaine...

We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel machines. Special cases considered are bagging and support vector machines. We present experimental results supporting the theoretical bounds, and describe characteristics of kernel machines ensembles suggested from the ...

This paper is an attempt to increase the understanding in the behavior of ensembles for discrete variables in a quantitative way. A set of tight upper and lower bounds for the accuracy of an ensemble is presented for wide classes of ensemble algorithms, including bagging and boosting. The ensemble accuracy is expressed in terms of the accuracies of the members of the ensemble. Since those bound...

Convolutional Neural Networks have achieved state-ofthe-art performance on a wide range of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling is typically treated as a post-hoc procedure implemented by averaging independently trained models with model variation induced by bagging or random initialization. In this paper, we rigorously treat ensembling as a fir...

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 two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitting. As against bagging, with these algorithms learners have visibility on the other learners and cooperate to get diversity, a characteristic that has proved to be an issue of major concern to ensemble models. Experime...

In this paper we present a new method for fusing classifiers output for problems with a number of classes M > 2. We extend the well-known Behavior Knowledge Space method with a hierarchical approach of the different cells. We propose to add the ranking information of the classifiers output for the combination. Each cell can be divided into new sub-spaces in order to solve ambiguities. We show t...

The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the Ada...