A Self-Organizing Model for Logic Regression


  • Stanley Jerry Farlow University of Maine


Logic Regression, GMDH algorithm, Self-organizing methods


Logic regression, as developed by Ruczinski, Kooperberg, and LeBlanc (Ruczinski, Kooperberg, and LeBlanc 2003) is a multivariable regression methodology that constructs logical relationships among Boolean predictor variables that best predicts a Boolean dependent variable.  More specifically, they find a regression model of the form g(E/Y)= b0+b1L1+...+bmLm  where both the coefficients b0,b1,...,bm and the logical expressions Lj, j=1,...,m  are determined.  The logical expressions  are logical relationships among the predictor variables, such as "X1,X2 are true but not X5"  , or "X3,X5,X7 are true but not X1 or X2".  In their paper, the authors investigate the use a simulated annealing algorithm.  In this paper, we use the Group Method of Data Handling (GMDH) to approach the problem.



Author Biography

Stanley Jerry Farlow, University of Maine

Professor of Mathematics

University of Maine


I have a Ph.D in mathematics and have been a professor of mathematics at the University of Maine for 42 years.   Before that I was a Lieutenant Commander in the Public Health Service at the National Institutes of Health in Washington, D.C.   I have published papers in operations research, statistics, partial differential equations, and control theory.  I have also written more than 10 textboos in mathematics, some translated into Japanese, Indonesian, and Russian.





How to Cite

Farlow, S. J. (2010). A Self-Organizing Model for Logic Regression. European Journal of Pure and Applied Mathematics, 3(2), 163–173. Retrieved from https://ejpam.com/index.php/ejpam/article/view/602



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