Hybrid ABC and black hole algorithm with genetic operators optimized SVM ensemble based diagnosis of breast cancer
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition
Link
https://link.springer.com/content/pdf/10.1007/s10044-023-01203-6.pdf
Reference57 articles.
1. Abdar M, Zomorodi-Moghadam M, Zhou X, Gururajan R, Tao X, Barua PD, Gururajan R (2020) A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recogn Lett 132:123–131. https://doi.org/10.1016/j.patrec.2018.11.004
2. Afolayan J, Adebiyi M, Arowolo M, Chakraborty A (2022, 06). Breast cancer detection using particle swarm optimization and decision tree machine learning technique. 61-83. https://doi.org/10.1007/978-981-16-8150-94
3. Barzaman K, Karami J, Zarei Z, Hosseinzadeh A, Kazemi MH, Moradi- Kalbolandi S, Farahmand L (2020) Breast cancer: biology, biomarkers, and treatments. Int Immunopharmacol 84:106535. https://doi.org/10.1016/j.intimp.2020.106535
4. Bhardwaj A, Tiwari A (2015) Breast cancer diagnosis using genetically optimized neural network model. Expert Syst Appl 42(10):4611–4620. https://doi.org/10.1016/j.eswa.2015.01.065
5. Boser BE, Guyon IM, Vapnik VN (1992) Training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual acm workshop on computational learning theory, pp 144–152. Publ by ACM. https://doi.org/10.1145/130385.130401
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