Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

2001 ◽  
Vol 9 (1) ◽  
pp. 88-101 ◽  
Author(s):  
Xia Hong ◽  
C.J. Harris
Keyword(s):  
Variable Selection ◽  
Operating Point ◽  
Selection Algorithm ◽  
Neurofuzzy Networks

scholarly journals A mixture of local and quadratic approximation variable selection algorithm in nonconcave penalized regression

2013 ◽  
Vol Volume 16, 2012 ◽  
Author(s):  
Assi N'GUESSAN ◽  
Ibrahim Sidi Zakari ◽  
Assi Mkhadri
Keyword(s):  
Variable Selection ◽  
Penalized Likelihood ◽  
Real Data ◽  
Penalized Regression ◽  
Penalty Functions ◽  
Selection Algorithm ◽  
Data Set ◽  
International Audience ◽  
One Step ◽  
Nonconvex Penalty

International audience We consider the problem of variable selection via penalized likelihood using nonconvex penalty functions. To maximize the non-differentiable and nonconcave objective function, an algorithm based on local linear approximation and which adopts a naturally sparse representation was recently proposed. However, although it has promising theoretical properties, it inherits some drawbacks of Lasso in high dimensional setting. To overcome these drawbacks, we propose an algorithm (MLLQA) for maximizing the penalized likelihood for a large class of nonconvex penalty functions. The convergence property of MLLQA and oracle property of one-step MLLQA estimator are established. Some simulations and application to a real data set are also presented.

scholarly journals Filter Variable Selection Algorithm Using Risk Ratios for Dimensionality Reduction of Healthcare Data for Classification

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 222 ◽  
Author(s):  
Bodur ◽  
Atsa’am
Keyword(s):  
Data Mining ◽  
Variable Selection ◽  
Feature Space ◽  
Selection Methods ◽  
Selection Algorithm ◽  
Fisher Score ◽  
Healthcare Data ◽  
Classification Tasks ◽  
Risk Ratios ◽  
Variable Ranking

This research developed and tested a filter algorithm that serves to reduce the feature space in healthcare datasets. The algorithm binarizes the dataset, and then separately evaluates the risk ratio of each predictor with the response, and outputs ratios that represent the association between a predictor and the class attribute. The value of the association translates to the importance rank of the corresponding predictor in determining the outcome. Using Random Forest and Logistic regression classification, the performance of the developed algorithm was compared against the regsubsets and varImp functions, which are unsupervised methods of variable selection. Equally, the proposed algorithm was compared with the supervised Fisher score and Pearson’s correlation feature selection methods. Different datasets were used for the experiment, and, in the majority of the cases, the predictors selected by the new algorithm outperformed those selected by the existing algorithms. The proposed filter algorithm is therefore a reliable alternative for variable ranking in data mining classification tasks with a dichotomous response.

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