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Article

An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

Ensemble learning can improve the accuracy of the classification algorithm and it has been widely used. Traditional ensemble learning methods include bagging, boosting and other methods, both of which are ensemble learning methods based on homogenous base classifiers, and obtain a diversity of base classifiers only through sample perturbation. However, heterogenous base classifiers tend to be more diverse, and multi-angle disturbances tend to obtain a variety of base classifiers. This paper

Fan Huimin, Li Pengpeng, Zhao Yingze, Li Danyang

International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 130–134

Article

RECOMMENDER ALGORITHMS BASED ON BOOSTING ENSEMBLE LEARNING

This article introduces ensemble learning algorithms in recommender systems, and in boosting algorithm framework of this article, shows how to filter the basic recommendation algorithm according to the characteristics of boosting algorithm. By comparing the rational choice of the two recommended boosting algorithm is applied to the frame. And then it determines the main parameters of the algorithm through the experiments, ultimately to obtain a more effective integration of the recommendation

Cheng Lili

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 368–386

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