Abstract
State of the art equation discovery systems are concerned with the empirical approach to modeling of physical systems, where none or a very limited portion of the expert knowledge about the observed system is used in the modeling process. In this paper, we propose a formalism for integration of the population dynamics modeling knowledge into the process of equation discovery. The formalism allows the encoding of a high-level domain knowledge accessible to human experts. The encoded knowledge can be automatically transformed into the operational form of context dependent grammars. We present an extended version of the equation discovery system Lagramge that can use these context free grammars. Experimental evaluation shows that the integration of domain knowledge in the process of equation discovery considerably improves the efficiency and noise robustness of Lagramge.
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Todorovski, 1., DŽeroski, S. (2001). Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery. In: De Raedt, L., Flach, P. (eds) Machine Learning: ECML 2001. ECML 2001. Lecture Notes in Computer Science(), vol 2167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44795-4_41
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DOI: https://doi.org/10.1007/3-540-44795-4_41
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