Abstract
Feature selection is an important process of high-dimensional data analysis in data mining and machine learning. In the feature selection stage, the cost of misclassification and the structural information of paired samples on each feature dimension are often ignored. To overcome this, we propose semi-supervised feature selection based on cost-sensitive and structural information. First, cost-sensitive learning is incorporated into the semi-supervised framework. Second, the structural information between a pair of samples in each feature dimension is encapsulated into the feature graph. Finally, the correlation between the candidate feature and the target feature is added, which avoids the misunderstanding of the feature with low correlation as the salient feature. Furthermore, the proposed method also considers the redundancy between feature pairs, which can improve the accuracy of feature selection. The proposed method is more interpretable and practical than previous semi-supervised feature selection algorithms, because it considers the misclassification cost, structural relationship and the correlations between features and target features. Experimental results show that the promising performance of the proposed method outperforms the state-of-the-arts on eight data sets.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant No: 81701780); the Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (No. 20-A-01-01); the Guangxi Natural Science Foundation (Grant No: 2017GXNSFBA198221); the Project of Guangxi Science and Technology (GuiKeAD20159041,GuiKeAD19110133); the Innovation Project of Guangxi Graduate Education (Grants No: JXXYYJSCXXM-011).
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Tao, Y., Lu, G., Ma, C., Su, Z., Hu, Z. (2021). Semi-supervised Feature Selection Based on Cost-Sensitive and Structural Information. In: Qiao, M., Vossen, G., Wang, S., Li, L. (eds) Databases Theory and Applications. ADC 2021. Lecture Notes in Computer Science(), vol 12610. Springer, Cham. https://doi.org/10.1007/978-3-030-69377-0_3
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