Introduction
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2096-9_1
Reference38 articles.
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2. Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., Aljaaf, A.J.: A systematic review on supervised and unsupervised machine learning algorithms for data science. In: Supervised and Unsupervised Learning for Data Science, pp. 3–21. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-22475-2_1
3. Bandaru, S., Deb, K.: Automated discovery of vital knowledge from Pareto-optimal solutions: first results from engineering design. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010). https://doi.org/10.1109/CEC.2010.5586501
4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
5. Brockhoff, D., Zitzler, E.: Are all objectives necessary? on dimensionality reduction in evolutionary multiobjective optimization. In: Runarsson, T.P., Beyer, H.G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) Parallel Problem Solving from Nature - PPSN IX, pp. 533–542. Springer, Berlin Heidelberg, Berlin, Heidelberg (2006)
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