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Elite bases regression: A real-time algorithm for symbolic regression
Chen C(陈辰)1,2; Luo ZT(罗长童)1; Jiang ZL(姜宗林)1,2
2017
会议名称13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
会议日期July 29, 2017 - July 31, 2017
会议地点Guilin, Guangxi, China
会议录名称ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
页码529-535
DOI10.1109/FSKD.2017.8393325
摘要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. © 2017 IEEE.
关键词Data mining Fuzzy systems Genetic algorithms Genetic programming Iterative methods Learning systems Comparative studies Correlation coefficient Function extraction Large scale problem Machine learning methods Real time algorithms Symbolic regression Symbolic regression problems
ISBN号9781538621653
收录类别EI
语种英语
引用统计
文献类型会议论文
条目标识符http://dspace.imech.ac.cn/handle/311007/78004
专题高温气体动力学国家重点实验室
作者单位1.State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing; 100190, China;
2.School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing; 100049, China
推荐引用方式
GB/T 7714
Chen C,Luo ZT,Jiang ZL. Elite bases regression: A real-time algorithm for symbolic regression[C],2017:529-535.
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