How to handle error bars in symbolic regression for data mining in scientific applications

A. Murari, E. Peluso, M. Gelfusa, M. Lungaroni, P. Gaudio

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015 - Egham, United Kingdom
Duration: 1 Jan 2015 → …

Conference

Conference3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015
CountryUnited Kingdom
CityEgham
Period1/1/15 → …

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Murari, A., Peluso, E., Gelfusa, M., Lungaroni, M., & Gaudio, P. (2015). How to handle error bars in symbolic regression for data mining in scientific applications. Paper presented at 3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015, Egham, United Kingdom. https://doi.org/10.1007/978-3-319-17091-6_29