2008 Fourth International Conference on Natural Computation (2008)
Oct. 18, 2008 to Oct. 20, 2008
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICNC.2008.640
Mining the interesting functions from the large scale data sets is an important task in KDD. Traditional gene expression programming (GEP) is a useful tool to discover functions. However, it cannot mine very complex functions. To resolve this problem, a novel method of function mining is proposed in this paper. The main contributions of this paper include: (1) analyzing the limitations of function mining based on traditional GEP, (2) proposing a nested function mining method based on GEP (GEP-NFM), and (3) experimental results suggest that the performance of GEP-NFM is better than that of the existing GEP-ADF. Averagely, compared with traditional GEP-ADF, the successful rate of GEP-NFM increases 20% and the number of evolving generations decrease 25%.
T. Li et al., "GEP-NFM: Nested Function Mining Based on Gene Expression Programming," 2008 Fourth International Conference on Natural Computation(ICNC), vol. 06, no. , pp. 283-287, 2008.