Less is More: Feature Choosing under Privacy-Preservation for Efficient Web Spam Detection
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-86475-0_1
Reference9 articles.
1. Zhuang, X., Zhu, Y., Peng, Q., Khurshid, F.: Using deep belief network to demote web spam. Fut. Gener. Comput. Syst. 118, 94–106 (2021)
2. Wang, Z., Hu, R., Chen, Q., Gao, P., Xu, X.: ColluEagle: collusive review spammer detection using Markov random fields. Data Min. Knowl. Disc. 34(6), 1621–1641 (2020). https://doi.org/10.1007/s10618-020-00693-w
3. Dou, Y., Ma, G., Yu, P., et al. Robust spammer detection by nash reinforcement learning. In: Proceedings of the 26th ACM SIGKDD Conference, pp. 924–933, San Diego (2020)
4. Lecture Notes in Computer Science;S Wei,2017
5. Lu, X.-Y., Chen, M.-S., Wu, J.-L., Chang, P.-C., Chen, M.-H.: A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection. Pattern Anal. Appl. 21(3), 741–754 (2017). https://doi.org/10.1007/s10044-017-0602-2
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