A real-coded genetic algorithm for constructive induction

被引:1
作者
HajAbedi, Z. [1 ]
机构
[1] Islamic Azad Univ Iran, Sci & Res Branch, Tehran, Iran
来源
2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5 | 2009年
关键词
D O I
10.1109/CEC.2009.4983191
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Constructive Induction (CI) is a process applied to representation space prior to learning algorithms. This process transforms original representation space into a representation that highlights regularities. In this new improved space learning algorithms work more effectively, generating better solutions. Most Cl methods apply a greedy strategy to improve representation space. Greedy methods might converge to local optima, when search space is complex. Genetic Algorithms (GA) as a global search strategy is more effective in such situations. In this paper, a real-coded GA (RGACI) model is represented for CI. This model optimizes the representation space by discretization of feature's values, constructing new features with a GA and evaluation and selection of features upon a PNN Classifier accuracy. Results reveal that PNN Classifier accuracy will improved considerably after it is integrated with RGACI model.
引用
收藏
页码:2036 / 2042
页数:7
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