NDLI: Maximum Likelihood Logistic Regression Using Metaheuristics

Content Provider	IEEE Xplore Digital Library
Author	Peterson, L.E.
Copyright Year	2009
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.
Starting Page	509
Ending Page	514
File Size	274019
Page Count	6
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.140
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Runtime Optimization methods Machine learning Cost function Covariance matrix Particle swarm optimization Ionosphere Logistics Genetic algorithms
Content Type	Text
Resource Type	Article

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