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.",