A Novel Function Mining Algorithm Based on Attribute Reduction and Improved Gene Expression Programming

IEEE Access

Song Deng¹  Guangwei Gao¹  Xiao Qin²  Changan Yuan²  Lechan Yang³ 

[1] Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China;School of Computer and Information Engineering, Nanning Normal University, Nanning, China;School of Software Engineering, Jinling Institute of Technology, Nanjing, China;

关键词: Gene expression programming;    attribute reduction;    function model mining;    dynamic population generation;    self-adaptive mutation;   

DOI  :  10.1109/ACCESS.2019.2911890

来源: DOAJ

【 摘 要 】

It is very interesting and important to determine the function model for remote-sensing data. The existing statistical and artificial intelligence models still have some defects. The statistical models rely heavily on prior knowledge and cannot objectively reflect the real function model contained in the remote-sensing data. In addition, the existing artificial intelligence models can very easily fall into the local optimum and have a low efficiency for high-dimensional remote-sensing data. In this paper, we first decrease the complexity of remote-sensing data by using rough sets and propose an attribute reduction algorithm based on rough sets for remote-sensing data (ARRS-RSD). On the basis of the algorithm, this paper presents a function mining algorithm for remote-sensing data by using gene expression programming and rough sets (FMRS-ARGEP). In FMRS-ARGEP, a dynamic population generation policy and a new mutation operation based on self-adaptive rate adjustment are introduced to improve the convergence of the algorithm. The experimental results show that the proposed algorithm outperforms traditional algorithms in terms of the average running time, the number of condition attributes after reduction, the attribute reduction ratio, the average convergence speed, the number of convergences, and the R² value of the model.

【 授权许可】

Unknown