Elite bases regression: A real-time algorithm for symbolic regression
文献类型：会议论文
| 作者 | Chen C(陈辰)1,2; Luo ZT(罗长童)1 ; Jiang ZL(姜宗林)1,2
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| 出版日期 | 2017 |
| 会议日期 | July 29, 2017 - July 31, 2017 |
| 会议地点 | Guilin, Guangxi, China |
| 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 |
| DOI | 10.1109/FSKD.2017.8393325 |
| 页码 | 529-535 |
| 英文摘要 | 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. |
| 会议录 | ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
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| 语种 | 英语 |
| ISBN号 | 9781538621653 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/78004]  |
| 专题 | 力学研究所_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]. 见:. Guilin, Guangxi, China. July 29, 2017 - July 31, 2017. |
入库方式： OAI收割
来源：力学研究所
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