- author = "Chen Chen and Changtong Luo and Zonglin Jiang",
- booktitle = "2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)",
- title = "Elite bases regression: A real-time algorithm for symbolic regression",
- year = "2017",
- pages = "529--535",
- abstract = "Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. However, its convergence speed might be too slow for large scale problems with a large number of variables. This drawback has become a bottleneck in practical applications. In this paper, a new non-evolutionary real-time algorithm for symbolic regression, Elite Bases Regression (EBR), is proposed. EBR generates a set of candidate basis functions coded with parse-matrix in specific mapping rules. Meanwhile, a certain number of elite bases are preserved and updated iteratively according to the correlation coefficients with respect to the target model. The regression model is then spanned by the elite bases. A comparative study between EBR and a recent proposed machine learning method for symbolic regression, Fast Function eXtraction (FFX), are conducted. Numerical results indicate that EBR can solve symbolic regression problems more effectively.",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/FSKD.2017.8393325",
- month = jul,
-
notes = "State Key Laboratory of High Temperature Gas Dynamics,
Institute of Mechanics, Chinese Academy of Sciences,
Beijing 100190, China
Also known as \cite{8393325}",
