A Hybrid Incremental Regression Neural Network for Uncertain Data Streams
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the International Joint Conference on Neural Networks, 2019, 2019-July
- Issue Date:
- 2019-07-01
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© 2019 IEEE. The design of classical regression algorithms was based on the assumption that all the required data is obtained at one time. With the emergence of big data, however, data is increasingly displayed in sequence form, such as in data streams, and can be read only once in a specific order. Many incremental regression algorithms which process data in a sequential manner have been proposed, but the accuracy of these algorithms deteriorates when the value of the data is uncertain. This paper proposes a hybrid incremental regression neural network based on self-organizing incremental neural network and incremental fuzzy support vector regression. In our proposed network, the neurons of the regression neural network are obtained by an improved self-organized incremental neural network (SOINN). This enables the regression neural network structure to self-organize as the number of neurons increases. An incremental fuzzy support vector regression (IFSVR) algorithm is then used to modify the parameters of the regression neural network. By combining the improved SOINN and IFSVR algorithms, our proposed hybrid incremental regression neural network is able to learn an accurate regression model from large uncertain data. Experiments on both artificial and real-world datasets indicate that our proposed hybrid incremental regression neural network achieves superior performance compared to other incremental regression algorithms.
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