Combining Conformal Prediction and Genetic Programming for Symbolic Interval Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4175

@InProceedings{Thuong:2017:GECCO,
  author =       "Pham Thi Thuong and Nguyen Xuan Hoai and Xin Yao",
  title =        "Combining Conformal Prediction and Genetic Programming
                 for Symbolic Interval Regression",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "1001--1008",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071280",
  DOI =          "doi:10.1145/3071178.3071280",
  acmid =        "3071280",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, conformal
                 prediction, interval prediction, linear quantile
                 regression, quantile regression, quantile regression
                 forests, symbolic regression",
  month =        "15-19 " # jul,
  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 forest. 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 forest on validity
                 and much better than them on efficiency.",
  notes =        "Also known as \cite{Thuong:2017:CCP:3071178.3071280}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Pham Thi Thuong Nguyen Xuan Hoai Xin Yao

Citations