This paper compares the accuracy of combined classifiers in medical data bases to the same knowledge discovery techniques applied to generic data bases. Specifically, we apply Bagging and Boosting methods for 16 medical and 16 generic data bases and compare the accuracy results with a more traditional approach (C4.5 algorithm). Bagging and Boosting methods are applied using different numbers of...

The idea of ensemble learning is to employ multiple learners and combine their predictions. There is no definitive taxonomy. Jain, Duin and Mao (2000) list eighteen classifier combination schemes; Witten and Frank (2000) detail four methods of combining multiple models: bagging, boosting, stacking and errorcorrecting output codes whilst Alpaydin (2004) covers seven methods of combining multiple...

Bagging and boosting are two popular ensemble methods that 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. Pasting small votes can utilize the power of b...

This paper examines the applicability of classifier combination approaches such as bagging and boosting for coreference resolution. To the best of our knowledge, this is the first effort that utilizes such techniques for coreference resolution. In this paper, we provide experimental evidence which indicates that the accuracy of the coreference engine can potentially be increased by use of baggi...

In this paper, bagging and boosting techniques are proposed as performing tools for churn prediction. These methods consist of sequentially applying a classification algorithm to resampled or reweigthed versions of the data set. We apply these algorithms on a customer database of an anonymous U.S. wireless telecom company. Bagging is easy to put in practice and, as well as boosting, leads to a ...

Machine Learning tools are increasingly being applied to analyze data from microarray experiments. These include ensemble methods where weighted votes of constructed base classifiers are used to classify data. We compare the performance of AdaBoost, bagging and BagBoost on gene expression data from the yeast cell cycle. AdaBoost was found to be more effective for the data than bagging. BagBoost...

We present an extension of GP (Genetic Programming) by means of resampling techniques, i.e., Bagging and Boosting. These methods both manipulate the training data in order to improve the learning algorithm. In theory they can signi cantly reduce the error of any weak learning algorithm by repeatedly running it. This paper extends GP by dividing a whole population into a set of subpopulations, e...

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. ...

This paper examines the applicability of classifier combination approaches such as bagging and boosting for coreference resolution. To the best of our knowledge, this is the first effort that utilizes such techniques for coreference resolution. In this paper, we provide experimental evidence which indicates that the accuracy of the coreference engine can potentially be increased by use of baggi...