On the other hand, estimation and prediction performance of model also taken into account for best subset chosen. 2000 Mathematics Subject Classifications: 62G08; 62J02; 17-08; 62B10; 62-07
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper we develop a novel nonparametric predictive subset regression modeling procedure that involves a combination of regression trees with radial basis function (RBF) neural networks hybridized with the genetic algorithm (GA) to carry out the subset selection of the best predictors.
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Key Findings & Data:We use the information-theoretic measure of complexity (ICOMP) criterion of [5, 6, 7, 8] as our fitness function to choose the best approximating radial basis functions and to choose the best subset of predictors with the GA.
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Conclusion & Impact:On the other hand, estimation and prediction performance of model also taken into account for best subset chosen. 2000 Mathematics Subject Classifications: 62G08; 62J02; 17-08; 62B10; 62-07
🔬Design:Modeling & Simulation
📍Scope:2000 Verileri
⚙️Variables:Domain Parameters
Abstract
In this paper we develop a novel nonparametric predictive subset regression modeling procedure that involves a combination of regression trees with radial basis function (RBF) neural networks hybridized with the genetic algorithm (GA) to carry out the subset selection of the best predictors. We use the information-theoretic measure of complexity (ICOMP) criterion of [5, 6, 7, 8] as our fitness function to choose the best approximating radial basis functions and to choose the best subset of predictors with the GA. To avoid the potential singularities in the design matrix, we combine our model with analytical global ridge regression for regularization. On the other hand, estimation and prediction performance of model also taken into account for best subset chosen. 2000 Mathematics Subject Classifications: 62G08; 62J02; 17-08; 62B10; 62-07
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Cite This Article
BOZDOGAN, H., AKBILGIC, O. (2022). Predictive subset selection using regression trees and RBF neural networks hybridized with the genetic algorithm. European Journal of Pure and Applied Mathematics (elektronik), Cilt: 4 - Sayı: 4, 467–485. https://ajindex.com/articles/511288
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