Abstract
Data mining and machine learning fields are facing with a great challenge of mass data with high dimensionality. Feature selection can contribute a lot to address this issue with the concept of reducing the number of features by eliminating the redundant and irrelevant ones while preserving the information of original features maximally. This paper analyzes and compares two common feature selection methods, then puts forward a novel method for feature selection based on information gain and BP neural network (IGBP). The experimental result shows that IGBP method can reduce the time cost and improve the accuracy of the model at the meantime. The scientificity and superiority of IGBP are demonstrated in this paper, making it an efficient approach to deal with high-dimensional data.
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Acknowledgements
This work was supported by The National Key Technology R&D Program of China (2015BAK36B04).
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Wang, X., Zuo, M., Song, L. (2018). A Feature Selection Method Based on Information Gain and BP Neural Network. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_3
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DOI: https://doi.org/10.1007/978-981-10-6496-8_3
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