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Combining conformal prediction and genetic programming for symbolic interval regression

Published: 01 July 2017 Publication History

Abstract

Symbolic regression has been one of the main learning domains for Genetic Programming. However, most work so far on using genetic programming for symbolic regression only focus on point prediction. The problem of symbolic interval regression is for each input to find a prediction interval containing the output with a given statistical confidence. This problem is important for many risk-sensitive domains (such as in medical and financial applications). In this paper, we propose the combination of conformal prediction and genetic programming for solving the problem of symbolic interval regression. We study two approaches called black-box conformal prediction genetic programming (black-box CPGP) and white-box conformal prediction genetic programming (white-box CPGP) on a number of benchmarks and previously used problems. We compare the performance of these approaches with two popular interval regressors in statistic and machine learning domains, namely, the linear quantile regression and quantile random forrest. The experimental results show that, on the two performance metrics, black-box CPGP is comparable to the linear quantile regression and not much worse than the quantile random forrest on validity and much better than them on efficiency.

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Cited By

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  • (2023)Single and Multi-objective Genetic Programming Methods for Prediction IntervalsArtificial Life and Evolutionary Computation10.1007/978-3-031-31183-3_17(205-218)Online publication date: 30-Apr-2023
  • (2022)Cartesian Genetic Programming: Some New DetectionsAdvances in Information and Communication10.1007/978-3-030-98015-3_20(294-313)Online publication date: 12-Mar-2022
  • (2020)Choosing function sets with better generalisation performance for symbolic regression modelsGenetic Programming and Evolvable Machines10.1007/s10710-020-09391-4Online publication date: 12-May-2020
  • Show More Cited By

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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
July 2017
1427 pages
ISBN:9781450349208
DOI:10.1145/3071178
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 July 2017

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Author Tags

  1. conformal prediction
  2. genetic programming
  3. interval prediction
  4. linear quantile regression
  5. quantile regression
  6. quantile regression forests
  7. symbolic regression

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  • Research-article

Funding Sources

  • Southern University of Science and Technology (SUSTech), Shenzhen, China
  • Vietnam National Foundation for Science and Technology Development (NAFOSTED)

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GECCO '17
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GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2023)Single and Multi-objective Genetic Programming Methods for Prediction IntervalsArtificial Life and Evolutionary Computation10.1007/978-3-031-31183-3_17(205-218)Online publication date: 30-Apr-2023
  • (2022)Cartesian Genetic Programming: Some New DetectionsAdvances in Information and Communication10.1007/978-3-030-98015-3_20(294-313)Online publication date: 12-Mar-2022
  • (2020)Choosing function sets with better generalisation performance for symbolic regression modelsGenetic Programming and Evolvable Machines10.1007/s10710-020-09391-4Online publication date: 12-May-2020
  • (2018)Analysing symbolic regression benchmarks under a meta-learning approachProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208293(1342-1349)Online publication date: 6-Jul-2018

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