Multiple classifier systems combine several individual classifiers to deliver a final classification decision. An increasingly controversial question is whether such systems can outperform the single best classifier and if so, what form of multiple classifier system yields the greatest benefit. In this paper the performance of several multiple classifier systems are evaluated in terms of their ...

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...

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...

Boosting is used to determine the order in which classifiers are aggregated in a bagging ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging ensemble allows the identification of subensembles that require less memory for storage, have a faster classification speed and can perform better than the original bagging ensemble. Furthermore, ensemble pruning does not ...

A combination of classification rules (classifiers) is known as an Ensemble, and in general it is more accurate than the individual classifiers used to build it. Two popular methods to construct an Ensemble are Bagging (Bootstrap aggregating) introduced by Breiman, [4] and Boosting (Freund and Schapire, [11]). Both methods rely on resampling techniques to obtain different training sets for each...

In classifier combination, it is believed that diverse ensembles have a better potential for improvement on the accuracy than nondiverse ensembles. We put this hypothesis to a test for two methods for building the ensembles: Bagging and Boosting, with two linear classifier models: the nearest mean classifier and the pseudo-Fisher linear discriminant classifier. To estimate diversity, we apply n...

Even the support vector machine (SVM) has been proposed to provide a good generalization performance, the classi6cation result of the practically implemented SVM is often far from the theoretically expected level because their implementations are based on the approximated algorithms due to the high complexity of time and space. To improve the limited classi6cation performance of the real SVM, w...

Breiman’s bagging and Freund and Schapire’s boosting are recent methods for improving the predictive power of classifier learning systems. Both form a set of classifiers that are combined by voting, bagging by generating replicated bootstrap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that l...

While the support vector machine (SVM) can provide a good generalization performance, the classification result of the SVM is often far from the theoretically expected level in practical implementation because they are based on approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use an SVM ensembl...