GEP-NFM: Nested Function Mining Based on Gene Expression Programming

被引：1
|
作者：
Li, Taiyong [1 ]
Tang, Changjie [1 ,2 ]
Wui, Jiang [1 ]
Wei, Xuzhong [1 ]
Li, Chuan [1 ]
Dai, Shucheng [1 ]
Zhu, Jun [3 ]
机构：
[1] Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China
[2] Southwestern Univ Finance & Econ, Sch Econ Informat Engn, Chengdu 610074, Peoples R China
[3] Sichuan Univ, Western China Med Sch, Birth Defects Supervising Ctr, Chengdu 610065, Peoples R China
来源：
ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 6, PROCEEDINGS | 2008年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/ICNC.2008.640
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 201116 and the number of evolving generations decrease 25%.
引用
收藏
页码：283 / +
页数：2
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