Paper Info
Title
Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm.
Abstract
There is a large scale initiative by the machine learning community to automate the design of machine learning techniques to remove reliance on the human expert, providing out of the box software that can be used by novices. In this study the automated design of genetic programming classification algorithms is proposed. A number of design decisions have to be considered by algorithm designers during the design process and this is usually a time consuming task. Our automated design approach uses a genetic algorithm to automatically configure a genetic programming classification algorithm. The genetic algorithm determines parameter values and sets the flow control for the classification algorithm. The proposed system is tested on real world problems and the results indicate that induced classifiers perform better than manually designed classifiers.
Year
DOI
Venue
2017
10.1007/978-3-319-55792-2_15
Lecture Notes in Computer Science
Keywords
Field
DocType
Data classification,Automated machine learning,Genetic programming,Genetic algorithms
Computer science,Meta-optimization,Genetic programming,Genetic representation,Artificial intelligence,Data classification,Statistical classification,Quality control and genetic algorithms,Population-based incremental learning,Genetic algorithm,Machine learning
Conference
Volume
ISSN
Citations 
10200
0302-9743
1
PageRank 
References 
Authors
0.34
18
2
Name
Order
Citations
PageRank
Thambo Nyathi110.34
Nelishia Pillay223733.72