Transformation of Input Space Using Statistical Moments: EA-Based Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  title =        "Transformation of Input Space Using Statistical
                 Moments: {EA}-Based Approach",
  author =       "Ahmed Kattan and Michael Kampouridis and 
                 Yew-Soon Ong and Khalid Mehamdi",
  pages =        "2499--2506",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "Genetic algorithms, Genetic programming",
  URL =          "",
  size =         "8 pages",
  DOI =          "doi:10.1109/CEC.2014.6900390",
  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.",
  notes =        "WCCI2014",

Genetic Programming entries for Ahmed Kattan Michael Kampouridis Yew-Soon Ong Khalid Mehamdi