Research on Feature Selection Methods Based on Feature Clustering and Information Theory
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5618-6_7
Reference17 articles.
1. Yan, X.Y., Nazmi, S., Erol, B.A., et al.: An efficient unsupervised feature selection procedure through feature clustering. Pattern Recogn. Lett. 131, 227–284 (2020)
2. Osama, S., Shaban, H., Ali, A.A.: Gene reduction and machine learning algorithms for cancer classification based on microarray gene expression data: a comprehensive review. Expert Syst. Appl. 213, 118946 (2023)
3. Lin, X.H., Li, C., Ren, W.J., Luo, X., Qi, Y.P.: A new feature selection method based on symmetrical uncertainty and interaction gain. Comput. Biol. Chem. 83, 107149 (2019)
4. Xu, J., Tang, B., He, H.B., et al.: Semisupervised feature selection based on relevance and redundancy criteria. IEEE Trans. Neural Networks Learn. Syst. 28(9), 1974–1984 (2017)
5. Gao, W.F., Hu, L., Zhang, P.: Feature redundancy term variation for mutual information-based feature selection. Appl. Intell. 50(8), 1272–1288 (2020)
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