Abstract
Feature selection is an important part of data preprocessing. Selecting effective feature subsets can effectively reduce feature redundancy and reduce irrelevant features, and reduce training costs. Based on the theory of feature clusters, this paper proposes a feature selection strategy based on the graph structure. Considering a feature as a node in the graph, using the idea of graph message propagation, integrating the first-order neighbor information of each node, and then selecting the key point of the local maximum score as the selected feature, this can effectively reduce the feature redundancy and reduce features that are not related to the label. Finally, in order to verify the anti-interference of this novel method, the noise dimension was added in the UCI data set, and the comparison test was again performed. The experimental results show that the proposed algorithm can effectively improve the classification accuracy in a specific data set, and the anti-interference is better than other feature selection algorithms.
Z. Zhang and D. Zheng—This work was supported by the Natural Science Foundation of Heilongjiang Province (No. F2017024, No. F2017025).
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Hu, Z., Zhang, Z., Huang, Z., Zheng, D., Zhang, Z. (2019). Feature Selection Based on Graph Structure. In: Li, Y., Cardei, M., Huang, Y. (eds) Combinatorial Optimization and Applications. COCOA 2019. Lecture Notes in Computer Science(), vol 11949. Springer, Cham. https://doi.org/10.1007/978-3-030-36412-0_23
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