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- W2358474461 abstract "Ensemble learning has become one of research fields of machine learning,it dramatically improves generalization performance of classifier.After analyzing ensemble approach to both Bagging and Adaboost,we point out their some flaws.Then we present a novel approach to neural network ensemble,called DBNNE below.In this method,a diverse data set is generated to increase ensemble diversity.Moreover,to ensure high accuracy of ensemble,we test performance of ensemble when a classifier is added to ensemble .Finally,we experiment on ten representative data sets.The results show that DBNNE achieves higher predictive accuracy than Bagging and AdaBoost on small data sets and comparable performance on larger data sets." @default.
- W2358474461 created "2016-06-24" @default.
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- W2358474461 date "2005-01-01" @default.
- W2358474461 modified "2023-09-24" @default.
- W2358474461 title "An Approach to Improving Diversity of Neural Network Ensemble" @default.
- W2358474461 hasPublicationYear "2005" @default.
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