Maximum likelihood logistic regression using metaheuristics

Leif E. Peterson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Maximum likelihood-based logistic regression coefficients and fitness growth rates for several metaheuristic techniques were compared with results from Newton-Raphson iteration. Metaheuristics included genetic algorithms (GA), covariance matrix self-adaptation evolution strategies (CMSA-ES), particle swarm optimization (PSO), and ant colony optimization (ACO). Results indicate that fitness growth rates for GA were greatly inferior to fitness values for NR, CMSA-ES, PSO, and ACO. For the data sets considered, coefficients determined using CMSA-ES were identical to coefficients generated with NR, while coefficients from PSO- and ACO-based logistic regression were only slightly different. For the ionosphere data with a larger number of features, ACO likelihood fitness growth was slower when compared with CMSA-ES and PSO. Because this was an early investigation of metaheuristics in logistic regression, future studies employing similar metaheuristics should focus on investigation of global vs. local minima.

Original languageEnglish (US)
Title of host publication8th International Conference on Machine Learning and Applications, ICMLA 2009
Pages509-514
Number of pages6
DOIs
StatePublished - Dec 1 2009
Event8th International Conference on Machine Learning and Applications, ICMLA 2009 - Miami Beach, FL, United States
Duration: Dec 13 2009Dec 15 2009

Other

Other8th International Conference on Machine Learning and Applications, ICMLA 2009
CountryUnited States
CityMiami Beach, FL
Period12/13/0912/15/09

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Software

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