Abstract
Feature selection is an important data analysis technique that used to reduce the redundancy of features and exploit hidden information in high-dimensional data. In this paper we propose a similarity metric based feature selection method named Fesim. We use the Euclidean distance to measure the similarity among all features, and then apply the density based DBSCAN algorithm to clustering features which to be relevant. Moreover, we present a strategy which choose representative features of each cluster accurately. We conducted comprehensive experiments to evaluate the proposed approach, and the results on different datasets are demonstrated its superiority.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China (Grant No. 61462012, No. 61562010, No. U1531246), the Innovation Team of the Data Analysis and Cloud Service of Guizhou Province (Grant No. [2015]53), Science and Technology Project of the Department of Science and Technology in Guizhou Province (Grant No. LH [2016]7427).
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Chen, Y., Li, H., Chen, M., Dai, Z., Li, H., Zhu, M. (2019). Enhancing Feature Selection with Density Cluster for Better Clustering. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational and Statistical Methods in Intelligent Systems. CoMeSySo 2018. Advances in Intelligent Systems and Computing, vol 859. Springer, Cham. https://doi.org/10.1007/978-3-030-00211-4_15
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DOI: https://doi.org/10.1007/978-3-030-00211-4_15
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