Genetic Algorithms Applied to Fractional Polynomials for Power Selection: Application to Diabetes Data

Year 2019, , 15 - 25, 31.08.2019

Barnabe Ndabashinze Gülesen Ustundag Siray , Luca Scrucca

https://doi.org/10.34110/forecasting.514761

Cited By: 2

Abstract

Fractional polynomials are powerful statistic tools used in
multivariable building model to select relevant variables and their functional
form. This selection of variables, together with their corresponding power is
performed through a multivariable fractional polynomials (MFP) algorithm that
uses a closed test procedure, called function selection procedure (FSP), based
on the statistical significance level α. In this paper, Genetic algorithms,
which are stochastic search and optimization methods based on string
representation of candidate solutions and various operators such as selection,
crossover and mutation; reproducing genetic processes in nature, are used as
alternative to MFP algorithm to select powers in an extended set of real
numbers (to be specified) by minimizing the Bayesian Information Criteria
(BIC). A simulation study and an application to a real dataset are performed to
compare the two algorithms in many scenarios. Both algorithms perform quite
well in terms of mean square error with Genetic algorithms that yied a more
parsimonious model comparing to MFP Algorithm.

Keywords

Fractional Polynomials, Genetic Algorithms, Function Selection Procedure

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