Abstract
Feature engineering has been widely used in the fields of pattern recognition, data mining and machine learning. Its main application is feature selection. As an excellent branch of feature selection, rough set attribute reduction has also been greatly developed in recent years, but classical rough set can only deal with discrete data. In view of this, combined with space partition, this paper proposes an efficient rough set attribute reduction theory, which makes the current model suitable for not only discrete data sets, but also continuous data sets and obtains an efficient rough set algorithm. The experimental results show that the algorithm reduces the number of attributes and uses the classifier KNN to test the accuracy of attribute reduction. Compared with the existing algorithms, it shows great advantages.
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Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant No. 2019QY(Y)0301, National Natural Science Foundation of China under Grant Nos. 61806030 and 61936001, the Natural Science Foundation of Chongqing under Grant Nos. cstc2019jcyj-msxmX0485 and cstc2019jcyj-cxttX0002 and by NICE: NRT for Integrated Computational Entomology, US NSF award 1631776.
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Wu, S., Xia, S., Chen, X. (2022). A Novel Space Division Rough Set Model for Feature Selection. In: Jain, L.C., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 297. Springer, Singapore. https://doi.org/10.1007/978-981-19-2448-4_7
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