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...

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...

Classi er committee learning methods generate multiple classi ers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the nal classication. Two such methods, Bagging and Boosting, have shown great success with decision tree learning. They create di erent classi ers by modifying the distribution of the training set. This paper stu...

We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multiple training sets. Other approaches, such as Randomized C4.5 apply randomization in selecting a test at a given node of a tree. Then there are approaches, such as random forests and random subspaces, that apply randomizat...

An ensemble consists of a set of independently trained classiiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classi-ers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund & Schapire 1996) are two relatively new but pop...

Under-sampling extensions of bagging are currently the most accurate ensembles specialized for class imbalanced data. Nevertheless, since improvements of recognition of the minority class, in this type of ensembles, are usually associated with a decrease of recognition of majority classes, we introduce a new, two phase, ensemble called Actively Balanced Bagging. The proposal is to first learn a...

Building the survivability prediction models is a challenging task because they provide an important approach to assessing risk and prognosis. In this paper, we investigated the performance of combining of the Bagging with several weak learners to build 5-accurate breast cancer survivability prediction models from the Srinagarind hospital database in Thailand. These models could assist medical ...

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...

Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring have been presented. In these studies, different ensemble schemes for complex classifiers were applied, and the best results were obtained using the Random Subspace method. The Bagging scheme was one of the ensemble methods used in the comparison. However, it was not correctly used. It is very important...

The relation of the Al-Alaoui pattern recognition algorithm to the boosting and bagging approaches to pattern recognition is delineated. It is shown that the Al-Alaoui algorithm shares with bagging and boosting the concepts of replicating and weighting instances of the training set. Additionally it is shown that the Al-Alaoui algorithm provides a Mean Square Error, MSE, asymptotic Bayesian appr...