Model Selection to Characterize Performance Using Genetic Algorithms

Martínez, D.; Dominguez, Jessica A.; Pena, Tomás F.; Rivera, Francisco F.; Blanco, Vicente

doi:10.1109/ispa.2012.134

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Genetic algorithm is used in [16] to search for the best candidate model to represent performance of the linear implementation of the broadcast collective communication in a cluster of multicores.…”

Section: Applying Genetic Algorithms For Model Selectionmentioning

confidence: 99%

A New Approach for Classifier Model Selection and Tuning Using Logistic Regression and Genetic Algorithms

Aly

2016

Arab J Sci Eng

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Logistic regression is an efficient machine learning procedure, and it is applied to build a mathematical model for classifying a certain input to a certain class among a number of preset classes. One of the main limitations of the standard classification approaches is the sensitivity to model structure, and another limitation is the sensitivity to the chosen value of regularization parameter λ that affects the estimation of the generalization error of candidate model, as any wrong value might cause underfitting or overfitting. In this research, a new approach for building a classifier model based on logistic regression is proposed, and the new algorithm depends on genetic algorithm to choose the effective model structure and also utilizes a proposed procedure to tune the regularization parameter aiming to find better model parameters. A case study for prediction of a diabetic patient using Pima Indian data set is included, and results showed the high effectiveness of the proposed approach as it reached a simpler model with a higher accuracy.

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Section: Applying Genetic Algorithms For Model Selectionmentioning

confidence: 99%