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

Created by W.Langdon from gp-bibliography.bib Revision:1.7866

@InProceedings{Kattan:2014:CEC,
• 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 Computation",
• year = "2014",
• month = "6-11 " # jul,
• editor = "Carlos A. {Coello Coello}",
• ISBN = "0-7803-8515-2",
• keywords = "Genetic algorithms, Genetic programming",
• URL = "http://kampouridis.net/papers/WCCI%202014_R.pdf",
• 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

Citations