Title :
Attribute Reduction Function Mining Algorithm Based on Gene Expression Programming
Author :
Yuan, Chang-an ; Tang, Chang-jie ; Zuo, Jie ; Chen, An-long ; Wen, Yuan-Guang
Author_Institution :
Coll. of Comput., Sichuan Univ., Chengdu
Abstract :
When mining non-linearity function with large number of variables, traditional methods cannot effectively reduce the conditional attributes. To solve the problem, this paper proposes GEP-ARFM model. The model includes the concepts of marginal gene, marginal fitness, and revised fitness and the algorithms of GEPAMF, GARFM-GEP, and SARFM-GEP. The comparison experiments show that (1) both GARFM-GEP and SARFM-GEP can effectively reduce the conditional attributes to find the best function expression. (2) The precision of function expression by using SARFM-GEP is approximate with using GARFM-GEP algorithm. (3) SARFM-GEP method is 300 times faster than GARFM-GEP in the case of 20 independent variables. (4) The fitness value of the function expression got by using GEP-ARFM model is 24.6% greater than the traditional method
Keywords :
data mining; data reduction; genetic algorithms; GARFM-GEP algorithm; GEP-ARFM model; attribute reduction function mining algorithm; best function expression; gene expression programming; marginal fitness; marginal gene; revised fitness; Blindness; Computer science education; Cybernetics; Educational institutions; Educational programs; Educational technology; Functional programming; Gene expression; Machine learning; Machine learning algorithms; Prediction methods; Programming profession; Attribute Reduction; Function Mining; Gene Expression Programming; Marginal Gene; Remnant Distribution;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258533
