Scalable classification of large data sets by parallel genetic programming

被引：0
|
作者：
Folino, G [1 ]
Pizzuti, C [1 ]
Spezzano, G [1 ]
机构：
[1] Univ Calabria, ISI, CNR, DEIS, I-87036 Arcavacata Di Rende, CS, Italy
来源：
DISTRIBUTED AND PARALLEL SYSTEMS : FROM INSTRUCTION PARALLELISM TO CLUSTER COMPUTING | 2000年 / 567卷
关键词：
data mining; parallel genetic programming; cellular automata;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A parallel genetic programming approach to data classification is presented. The method uses cellular automata as a framework to enable a fine-grained parallel implementation of GP through the grid model. Experiments on real datasets from the UCI machine learning repository show good results with respect to C4.5. The generated trees are smaller, they have a misclassification error on the training set comparable, but, more important, they generalise better than C4.5: Furthermore, performance results show a nearly linear speedup.
引用
收藏
页码：87 / 90
页数：4
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