Combining Conformal Prediction and Genetic Programming for Symbolic Interval Regression

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@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",
• month = "15-19 " # jul,
• 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",
• URL = "http://www.cmap.polytechnique.fr/~nikolaus.hansen/proceedings/2017/GECCO/proceedings/proceedings_files/pap447s3-file1.pdf",
• URL = "http://doi.acm.org/10.1145/3071178.3071280",
• DOI = "doi:10.1145/3071178.3071280",
• acmid = "3071280",
• size = "8 pages",
• 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 Thuong Pham Thi Nguyen Xuan Hoai Xin Yao

Citations