Divide and Conquer: A Quick Scheme for Symbolic Regression
| Autor: | Zonglin Jiang, Chen Chen, Changtong Luo |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | International Journal of Computational Methods. 19 |
| ISSN: | 1793-6969 0219-8762 |
| DOI: | 10.1142/s0219876221420020 |
| Popis: | Symbolic regression (SR), as a special machine learning method, can produce mathematical models with explicit expressions. It has received increasing attention in recent years. However, finding a concise, accurate expression is still challenging because of its huge search space. In this work, a divide and conquer (D&C) scheme is proposed. It tries to divide the search space into a number of orthogonal sub-spaces based on the separability feature inferred from the sample data (dividing process). For each sub-space, a sub-function is learned (conquering process). The target model function is then reconstructed with the sub-functions according to their separability patterns. To this end, a separability pattern detecting technique, bi-correlation test (Bi-CT), is also proposed. Note that the sub-functions could be determined by any of the existing SR methods, which makes D&C easy to use. The D&C powered SR has been tested on many symbolic regression problems, and the study shows that D&C can help SR to get the target function more quickly and reliably. |
| Databáze: | OpenAIRE |
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