Transformation of Input Space Using Statistical Moments: EA-Based Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Kattan:2014:CEC,
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title = "Transformation of Input Space Using Statistical
Moments: {EA}-Based Approach",
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author = "Ahmed Kattan and Michael Kampouridis and
Yew-Soon Ong and Khalid Mehamdi",
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pages = "2499--2506",
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booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary
Computation",
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year = "2014",
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month = "6-11 " # jul,
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editor = "Carlos A. {Coello Coello}",
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address = "Beijing, China",
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ISBN = "0-7803-8515-2",
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keywords = "Genetic algorithms, Genetic programming",
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URL = "http://kampouridis.net/papers/WCCI%202014_R.pdf",
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size = "8 pages",
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DOI = "doi:10.1109/CEC.2014.6900390",
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abstract = "Reliable regression models in the field of Machine
Learning (ML) revolve around the fundamental property
of generalisation. This ensures that the induced model
is a concise approximation of a data-generating process
and performs correctly when presented with data that
have not been used during the learning process.
Normally, the regression model is presented with n
samples from an input space; that is composed of
observational data of the form (xi, y(xi)), i = 1...n
where each xi denotes a k dimensional input vector of
design variables and y is the response. When k n, high
variance and over-fitting become a major concern. In
this paper we propose a novel approach to mitigate this
problem by transforming the input vectors into new
smaller vectors (called Z set) using only a set of
simple statistical moments. Genetic Algorithm (GA) has
been used to evolve a transformation procedure. It is
used to optimise an optimal sequence of statistical
moments and their input parameters. We used Linear
Regression (LR) as an example to quantify the quality
of the evolved transformation procedure. Empirical
evidences, collected from benchmark functions and
real-world problems, demonstrate that the proposed
transformation approach is able to dramatically improve
LR generalisation and make it outperform other state of
the art regression models such as Genetic Programming,
Kriging, and Radial Basis Functions Networks. In
addition, we present an analysis to shed light on the
most important statistical moments that are useful for
the transformation process.",
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notes = "WCCI2014",
- }
Genetic Programming entries for
Ahmed Kattan
Michael Kampouridis
Yew-Soon Ong
Khalid Mehamdi
Citations