NDLI: Genetic programming for feature construction and selection in classification on high-dimensional data

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Genetic programming for feature construction and selection in classification on high-dimensional data

Content Provider	Springer Nature Link
Author	Tran, Binh Xue, Bing Zhang, Mengjie
Copyright Year	2015
Abstract	Classification on high-dimensional data with thousands to tens of thousands of dimensions is a challenging task due to the high dimensionality and the quality of the feature set. The problem can be addressed by using feature selection to choose only informative features or feature construction to create new high-level features. Genetic programming (GP) using a tree-based representation can be used for both feature construction and implicit feature selection. This work presents a comprehensive study to investigate the use of GP for feature construction and selection on high-dimensional classification problems. Different combinations of the constructed and/or selected features are tested and compared on seven high-dimensional gene expression problems, and different classification algorithms are used to evaluate their performance. The results show that the constructed and/or selected feature sets can significantly reduce the dimensionality and maintain or even increase the classification accuracy in most cases. The cases with overfitting occurred are analysed via the distribution of features. Further analysis is also performed to show why the constructed feature can achieve promising classification performance.
Starting Page	3
Ending Page	15
Page Count	13
File Format	PDF
ISSN	18659284
Journal	Memetic Computing
Volume Number	8
Issue Number	1
e-ISSN	18659292
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-12-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Genetic programming Feature construction Feature selection Classification High-dimensional data ApplicationMathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Control, Robotics, Mechatronics Bioinformatics Applications of Mathematics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Science

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