Author:
Keren Liron Simon,Liberzon Alex,Lazebnik Teddy
Abstract
AbstractDiscovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.
Publisher
Springer Science and Business Media LLC
Reference116 articles.
1. Rip, A. & van der Meulen, B. J. R. The post-modern research system. Science and Public Policy 23, 343–352 (1996).
2. Miller, D. C. & Salkind, N. J. Handbook of Research Design and Social Measurement (Sage Publishing, 2002).
3. Sobh, R. & Perry, C. Research design and data analysis in realism research. Eur. J. Mark. 40, 1194–1209 (2006).
4. Michopoulos, J. & Lambrakos, S. On the fundamental tautology of validating data-driven models and simulations. In 5th International Conference, vol. 3515, 1194–1209 (Atlanta, GA, USA, 2005).
5. Chua, W. et al. Data-driven discovery and validation of circulating blood-based biomarkers associated with prevalent atrial fibrillation. Eur. Heart J. 40, 1268–1276 (2019).
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献