Innovative Incremental K-Prototypes Based Feature Selection for Medicine and Healthcare Applications
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3311-2_25
Reference18 articles.
1. Gorrab, S., Rejab, F.B., Nouira, K.: Advanced incremental attribute learning clustering algorithm for medical and healthcare applications. In: International Work-Conference on Bioinformatics and Biomedical Engineering, pp. 171–183. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07704-3_14
2. Almusallam, N., Tari, Z., Chan, J., Fahad, A., Alabdulatif, A., Al-Naeem, M.: Towards an Unsupervised Feature Selection Method for Effective Dynamic Features. IEEE Access 9, 77149–77163 (2021)
3. Gorrab, S., Rejab, F.B., Nouira, K.: Real-Time K-prototypes for incremental attribute learning using feature selection. In: Alyoubi, B., Ben Ncir, CE., Alharbi, I., Jarboui, A. (eds.) Machine Learning and Data Analytics for Solving Business Problems, pp. 165–187. Springer, Cham (2022)
4. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining Knowl. Discovery 2(3), 283–304 (1998)
5. Dhara, A. K., Mukhopadhyay, S., Dutta, A., Garg, M., Khandelwal, N.: Content-based image retrieval system for pulmonary nodules: assisting radiologists in self-learning and diagnosis of lung cancer. J. Digital Imaging 30(1), 63–77 (2017)
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