Accelerated Splitting Augmented Lagrange Method Based on Sparse Logistic Regression

2021 ◽  
Vol 11 (12) ◽  
pp. 2116-2123
Author(s):  
璇 周
Keyword(s):  
Logistic Regression ◽  
Lagrange Method ◽  
Sparse Logistic Regression ◽  
Augmented Lagrange Method

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Electronic Nose for Bladder Cancer Detection

2021 ◽  
Vol 5 (1) ◽  
pp. 22
Author(s):  
Heena Tyagi ◽  
Emma Daulton ◽  
Ayman S. Bannaga ◽  
Ramesh P. Arasaradnam ◽  
James A. Covington
Keyword(s):  
Bladder Cancer ◽  
Logistic Regression ◽  
Random Forest ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Electronic Nose ◽  
Random Forest Classifier ◽  
Urine Samples ◽  
Sparse Logistic Regression ◽  
High Separation

This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.

scholarly journals A note on embeddings for the Augmented Lagrange Method

2010 ◽  
Vol 20 (2) ◽  
pp. 183-196 ◽  
Author(s):  
Gemayqzel Bouza-Allende ◽  
Jurgen Guddat
Keyword(s):  
Augmented Lagrangian ◽  
Equality Constraints ◽  
Embedding Problem ◽  
Lagrange Method ◽  
Parametric Problem ◽  
Nonlinear Programs ◽  
Augmented Lagrange Method ◽  
Quadratic Perturbation ◽  
Lagrangian Embedding

Nonlinear programs (P) can be solved by embedding problem P into one parametric problem P(t), where P(1) and P are equivalent and P(0), has an evident solution. Some embeddings fulfill that the solutions of the corresponding problem P(t) can be interpreted as the points computed by the Augmented Lagrange Method on P. In this paper we study the Augmented Lagrangian embedding proposed in [6]. Roughly speaking, we investigated the properties of the solutions of P(t) for generic nonlinear programs P with equality constraints and the characterization of P(t) for almost every quadratic perturbation on the objective function of P and linear on the functions defining the equality constraints.

scholarly journals Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features

2018 ◽  
Vol 45 (9) ◽  
pp. 4112-4124 ◽  
Author(s):  
Hoda Nemat ◽  
Hamid Fehri ◽  
Nasrin Ahmadinejad ◽  
Alejandro F. Frangi ◽  
Ali Gooya
Keyword(s):  
Logistic Regression ◽  
Texture Features ◽  
Breast Lesions ◽  
Sparse Logistic Regression
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