منابع مشابه
Using boosting to prune bagging ensembles
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 ...
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Bagging, boosting and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-regressor. In this work, we built an ensemble of bagging, boosting and random subspace methods ensembles with 8 sub-regressors in each one and then an averaging methodology is used for the final prediction. We ...
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Classifying handwriting into a writer demographic category, e.g., gender, age, or handedness of the writer, is useful for more detailed analysis such as writer verification and identification. This paper describes classification into binary demographic categories using document macro features and several different classification methods: a single feed-forward neural network classifier and combi...
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The experiments aimed to compare three methods to create ensemble models implemented in a popular data mining system WEKA, were carried out. Six common algorithms comprising two neural network algorithms, two decision trees for regression, and linear regression and support vector machine were used to generate individual committees. All algorithms were employed to actual data sets derived from t...
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Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classiier learning systems. Both form a set of classiiers that are combined by voting, bagging by generating replicated boot-strap 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 le...
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ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2007
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2006.06.018