Modeling lab
CH-315
2.11. Feature selection
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Description
Having more features is not always better: we discuss the problem and potential solutions.
Page content
Lecture
Slides
Additional Resources and References
(1) Saeys, Y.; Inza, I.; Larranaga, P. A Review of Feature Selection Techniques in Bioinformatics. Bioinformatics 2007, 23 (19), 2507–2517. https://doi.org/10.1093/bioinformatics/btm344.
(2) Guyon, I.; Elisseeff, A. An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research 2003, 3, 1157–1182.
(3) Janet, J. P.; Kulik, H. J. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships. J. Phys. Chem. A 2017, 121 (46), 8939–8954. https://doi.org/10.1021/acs.jpca.7b08750.
(4) Ghiringhelli, L. M.; Vybiral, J.; Ahmetcik, E.; Ouyang, R.; Levchenko, S. V.; Draxl, C.; Scheffler, M. Learning Physical Descriptors for Materials Science by Compressed Sensing. New J. Phys. 2017, 19 (2), 023017. https://doi.org/10.1088/1367-2630/aa57bf.
(5) Ghiringhelli, L. M.; Vybiral, J.; Levchenko, S. V.; Draxl, C.; Scheffler, M. Big Data of Materials Science: Critical Role of the Descriptor. Physical Review Letters 2015, 114 (10). https://doi.org/10.1103/PhysRevLett.114.105503.
(6) Bartel, C. J.; Sutton, C.; Goldsmith, B. R.; Ouyang, R.; Musgrave, C. B.; Ghiringhelli, L. M.; Scheffler, M. New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides. Sci. Adv. 2019, 5 (2), eaav0693. https://doi.org/10.1126/sciadv.aav0693.