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...

Much attention has been given in machine learning field to the study of numerous resampling techniques during the last fifteen years. In the paper the investigation of m-out-of-n bagging with and without replacement and repeated cross-validation using genetic fuzzy systems is presented. All experiments were conducted with real-world data derived from a cadastral system and registry of real esta...

The motivation for this study was to learn to predict forest fires in Slovenia using different data mining techniques. We used predictive models based on data from a GIS (geographical information system), the weather prediction model Aladin and MODIS satellite data. We examined three different datasets: one only for the Kras region, one for whole Primorska region and one for continental Sloveni...

In recent years, together with bagging [5] and the random subspace method [15], boosting [6] became one of the most popular combining techniques that allows us to improve a weak classifier. Usually, boosting is applied to Decision Trees (DT’s). In this paper, we study boosting in Linear Discriminant Analysis (LDA). Simulation studies, carried out for one artificial data set and two real data se...

Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms, that are capable of processing those data streams on the fly. We propose online bagging, using an incremental matrix factorization algorithm ...

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 ...

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. ...

In this paper a novel ensemble based techniques for face recognition is presented. In ensemble learning a group of methods are employed and their results are combined to form the final results of the system. Gaining the higher accuracy rate is the main advantage of this system. Two of the most successful wrapping classification methods are bagging and boosting. In this paper we used the K neare...

Classification is an active topic of Machine Learning. The most recent achievements in this domain suggest using ensembles of learners instead of a single classifier to improve classification accuracy. Comparisons between Bagging and Boosting show that classifier ensembles perform better when their members exhibit diversity, that is commit different errors. This paper proposes a genetic algorit...