@article{A Self-Organizing Model for Logic Regression_2010, place={Maryland, USA}, volume={3}, url={https://www.ejpam.com/index.php/ejpam/article/view/602}, abstractNote={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.Â More specifically, they find a regression model of the form g(E/Y)= b0+b1L1+...+bmLmÂ Â where both the coefficients b0,b1,...,bmÂ and the logical expressions Lj, j=1,...,m Â are determined.Â The logical expressionsÂ Â are logical relationships among the predictor variables, such as "X1,X2Â are true but not X5"Â Â , or "X3,X5,X7 are true but not X1Â or X2".Â In their paper, the authors investigate the use a simulated annealing algorithm.Â In this paper, we use the Group Method of Data Handling (GMDH) to approach the problem.Â Â }, number={2}, journal={European Journal of Pure and Applied Mathematics}, year={2010}, month={Apr.}, pages={163–173} }