Combining conformal prediction and genetic programming for symbolic interval regression

Thuong，Pham Thi1; Hoai，Nguyen Xuan2; Yao，Xin3

10.1145/3071178.3071280

2017-07-01

GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

1001-1008

Berlin, Germany

1515 BROADWAY, NEW YORK, NY 10036-9998 USA

Association for Computing Machinery, Inc

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 con-formal 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, blackbox 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.

Conformai prediction Genetic programming Interval prediction Linear quantité regression Quantile regression Quantile regression forests Symbolic regression

其他

英语

EI ; CPCI-S

Vietnam National Foundation for Science and Technology Development (NAFOSTED)[102.01-2014.09]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Computer Science, Theory & Methods

WOS:000530095200126

20173104005624

Forecasting ; Genetic algorithms ; Learning systems ; Problem solving ; Regression analysis

Computer Programming:723.1 ; Mathematical Statistics:922.2

2-s2.0-85026420468

Scopus

1.IT Department,University of Information and Communication Technology,Thainguyen,Viet Nam
2.HANU IT R and D Center,HanoiUniversity,Hanoi,Viet Nam
3.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Thuong，Pham Thi,Hoai，Nguyen Xuan,Yao，Xin. Combining conformal prediction and genetic programming for symbolic interval regression[C]. 1515 BROADWAY, NEW YORK, NY 10036-9998 USA:Association for Computing Machinery, Inc,2017:1001-1008.