Abstract
In this study, we propose a framework to combine multiple classifiers in an ensemble system. By using the concept of information granularity, the interval membership values of each category of prediction are constructed from the numerical membership of observation data, instead of the combination of numerical membership values. To predict the class label of a new data, the weighted distances between the output of the base classifiers and the granularity prototypes of the observed object are calculated firstly. Then the predicted class label is obtained by selecting the label of the prototype with the shortest distance. In the experiments, we combine several algorithms to construct an ensemble classifier, and prove its excellent performance on UCI data set.
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Acknowledgment
This work is supported by the Natural Science Foundation of China (No. 61803065), the Fundamental Research Funds for the Central Universities (No.3132019602).
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Wang, X., Zhong, R. (2021). A New Weighted Ensemble Classifier Based on Granular Model. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_93
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DOI: https://doi.org/10.1007/978-3-030-70665-4_93
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