Much current research is undertaken into combining classifiers to increase the classification accuracy. We show, by means of an enumerative example, how combining classifiers can lead to much greater or lesser accuracy than each individual classifier. Measures of diversity among the classifiers taken from the literature are shown to only exhibit a weak relationship with majority vote accuracy. ...

An ensemble consists of a set of independently trained classifiers (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 classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (F’reund & Schapire 1996) are two relatively new but ...

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...

Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of the dataset can be a bottleneck. Voting many classifiers built on small subsets of data (“pasting small votes”) is a promising approach for learning from massive datasets, one that can utilize the power of bo...

W e study the effectiveness of three neural network ensembles in improving OCR performance: ( i ) Basic, (ii) Bagging, and (iii) Boosting. Three random character degradation models are introduced in training indivadual networks in order to reduce error correlation between individual networks and to improve the generalization ability of neural networks. We compare the recognition accuracies of t...

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...

Bagging, boosting and Random Forests are classical ensemble methods used to improve the performance of single classifiers. They obtain superior performance by increasing the accuracy and diversity of the single classifiers. Attempts have been made to reproduce these methods in the more challenging context of evolving data streams. In this paper, we propose a new variant of bagging, called lever...