Improving Generalisation of Genetic Programming for High-Dimensional Symbolic Regression with Feature Selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @InProceedings{Chen:2016:CEC,
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author = "Qi Chen and Bing Xue and Ben Niu and Mengjie Zhang",
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title = "Improving Generalisation of Genetic Programming for
High-Dimensional Symbolic Regression with Feature
Selection",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "3793--3800",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7744270",
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abstract = "Feature selection is a desired process when learning
from high-dimensional data. However, it is seldom
considered in Genetic Programming (GP) for
high-dimensional symbolic regression. This work aims to
develop a new method, Genetic Programming with Feature
Selection (GPWFS), to improve the generalisation
ability of GP for symbolic regression. GPWFS is a
two-stage method. The main task of the first stage is
to select important/informative features from fittest
individuals, and the second stage uses a set of
selected features, which is a subset of original
features, for regression. To investigate the
learning/optimisation performance and generalisation
capability of GPWFS, a set of experiments using
standard GP as a baseline for comparison have been
conducted on six real-world high-dimensional symbolic
regression datasets. The experimental results show that
GPWFS can have better performance both on the training
sets and the test sets on most cases. Further analysis
on the solution size, the number of distinguished
features and total number of used features in the
evolved models shows that using GPWFS can induce more
compact models with better interpretability and lower
computational costs than standard GP.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Qi Chen
Bing Xue
Ben Niu
Mengjie Zhang
Citations