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.
This is a post-peer-review, pre-copyedit version of an article published in 'Memetic Computing'. The final authenticated version is available online at: https://doi.org/10.1007/s12293-015-0173-y. The following terms of use apply: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use.
Genetic programming for feature construction and selection in classification on high-dimensional data
