How to Handle Error Bars in Symbolic Regression for Data Mining in Scientific Applications

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• 作者：A. Murari (7)
  E. Peluso (8)
  M. Gelfusa (8)
  M. Lungaroni (8)
  P. Gaudio (8)
  
  7. Consorzio RFX-Associazione EURATOM-ENEA per la Fusione ; Corso Stati Uniti ; 4 ; 35127 ; Padova ; Italy
  8. Associazione EURATOM-ENEA - University of Rome 鈥淭or Vergata鈥? Via del Politecnico 1 ; 00133 ; Rome ; Italy
  
• 关键词：Genetic Programming ; Symbolic regression ; Geodesic distance ; Scaling laws
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9047
• 期：1
• 页码：347-355
• 全文大小：285 KB
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  9. Murari, A., et al.: Nucl. Fusion 53, 043001 (2013), doi:10.1088/0029-5515/53/4/043001
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• 作者单位：Statistical Learning and Data Sciences
• 丛书名：978-3-319-17090-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Symbolic regression via genetic programming has become a very useful tool for the exploration of large databases for scientific purposes. The technique allows testing hundreds of thousands of mathematical models to find the most adequate to describe the phenomenon under study, given the data available. In this paper, a major refinement is described, which allows handling the problem of the error bars. In particular, it is shown how the use of the geodesic distance on Gaussian manifolds as fitness function allows taking into account the uncertainties in the data, from the beginning of the data analysis process. To exemplify the importance of this development, the proposed methodological improvement has been applied to a set of synthetic data and the results have been compared with more traditional solutions.