Paper Info
Title
Data classification using genetic parallel programming
Abstract
A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms.
Year
DOI
Venue
2003
10.1007/3-540-45110-2_88
GECCO
Keywords
Field
DocType
data classification,parallel algorithm,parallel hardware fitness evaluation,genetic parallel programming,proposed gpp-classifier,existing classification algorithm,simple classification program,data classification problem,parallel program,novel linear genetic programming,machine learning,genetics
Parallel algorithm,Computer science,Parallel computing,Genetic programming,Artificial intelligence,Data classification,Linear genetic programming,Statistical classification,Machine learning
Conference
Volume
ISSN
ISBN
2724
0302-9743
3-540-40603-4
Citations 
PageRank 
References 
3
0.45
2
Authors
3
Name
Order
Citations
PageRank
Sin Man Cheang1445.14
Kin Hong Lee2506.56
Kwong-Sak Leung31887205.58